AITL (Artificial Intelligence Training Layer) Architecture

AITL Overview

Savitri Network implements AITL (Artificial Intelligence Training Layer), a federated AI training platform that enables developers to distribute AI models across the network, leverage federated learning for collective model improvement, and use blockchain technology for data provenance and reward distribution. AITL transforms Savitri into a decentralized AI training infrastructure where users can contribute to model development while maintaining data privacy and earning rewards.

Technology Choice Rationale

Why Federated AI Training Layer

Problem Statement: Traditional AI model development requires centralized data collection, creating privacy concerns, data silos, and limiting access to diverse training datasets. Developers struggle to distribute models and users cannot participate in AI improvement while maintaining data ownership.

Chosen Solution: Decentralized federated learning platform with IPFS storage, blockchain verification, and reward distribution for data contributors.

Rationale:

• Privacy Preservation: User data remains local, only model improvements are shared
• Collective Intelligence: Multiple users contribute to better model performance
• Data Provenance: Blockchain verifies origin and authenticity of training contributions
• Incentive Alignment: Rewards distributed fairly to data providers and trainers
• Scalability: Distributed training reduces computational burden on individual developers

Expected Results:

• 10-100x larger and more diverse training datasets through network participation
• Improved model accuracy through federated learning (20-40% performance gains)
• Verifiable data provenance and contribution tracking
• Sustainable ecosystem for AI development with fair reward distribution
• Privacy-compliant AI training respecting user data ownership

Why IPFS + Blockchain Integration

Problem Statement: Centralized model storage creates single points of failure, version control challenges, and difficulty in verifying model authenticity and training history.

Chosen Solution: IPFS for distributed model storage combined with blockchain for verification, versioning, and reward coordination.

Rationale:

• Decentralized Storage: IPFS provides resilient, distributed model storage
• Content Addressing: Cryptographic hashes ensure model integrity
• Version Control: Blockchain tracks model versions and training history
• Verification: Smart contracts verify model authenticity and data provenance
• Reward Distribution: Blockchain enables transparent reward allocation

Expected Results:

• Tamper-proof model storage and versioning system
• Verifiable training history and model lineage
• Efficient distributed storage without central dependencies
• Transparent reward distribution based on actual contributions
• Reduced storage costs through content-addressed deduplication

AITL Architecture

Core Components

pub struct AITLEngine {
    pub model_distributor: ModelDistributor,    // Model distribution
    pub federated_trainer: FederatedTrainer,    // Federated learning
    pub ipfs_storage: IPFSStorage,              // IPFS integration
    pub blockchain_verifier: BlockchainVerifier, // Blockchain verification
    pub reward_manager: RewardManager,          // Reward distribution
    pub data_provenance: DataProvenance,        // Data provenance tracking
    pub config: AITLConfig,                     // Configuration
}

pub struct ModelDistributor {
    pub models: HashMap<ModelId, DistributedModel>, // Distributed models
    pub model_registry: ModelRegistry,          // Model registry
    pub version_manager: ModelVersionManager,   // Version management
    pub distribution_tracker: DistributionTracker, // Distribution tracking
}

#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub struct ModelId {
    pub developer: [u8; 32],                    // Developer address
    pub name: String,                           // Model name
    pub version: String,                        // Model version
    pub model_type: ModelType,                  // Model type
}

#[derive(Debug, Clone, PartialEq, Eq)]
pub enum ModelType {
    NeuralNetwork,                             // Neural network models
    RandomForest,                              // Random forest models
    GradientBoosting,                          // Gradient boosting models
    SupportVectorMachine,                      // SVM models
    Transformer,                               // Transformer models
    Custom(String),                           // Custom model types
}

#[derive(Debug, Clone)]
pub struct DistributedModel {
    pub id: ModelId,                           // Model ID
    pub model_type: ModelType,                  // Model type
    pub architecture: ModelArchitecture,        // Model architecture
    pub parameters: ModelParameters,            // Model parameters
    pub training_config: TrainingConfig,        // Training configuration
    pub ipfs_hash: String,                      // IPFS content hash
    pub blockchain_ref: String,                 // Blockchain reference
    pub version_history: Vec<ModelVersion>,     // Version history
    pub performance_metrics: ModelMetrics,       // Performance metrics
    pub reward_config: RewardConfig,            // Reward configuration
}

Federated Learning Engine

pub struct FederatedTrainer {
    pub local_models: HashMap<ModelId, LocalModel>, // Local models
    pub training_data: LocalTrainingData,      // Local training data
    pub federated_coordinator: FederatedCoordinator, // Federated coordination
    pub privacy_manager: PrivacyManager,       // Privacy management
    pub contribution_tracker: ContributionTracker, // Contribution tracking
}

impl FederatedTrainer {
    pub fn train_local_model(&mut self, model_id: &ModelId, user_data: &UserData) -> Result<TrainingResult, TrainingError> {
        // 1. Load base model from IPFS
        let base_model = self.load_model_from_ipfs(model_id)?;
        
        // 2. Prepare local training data
        let training_data = self.prepare_local_training_data(user_data)?;
        
        // 3. Train model locally with privacy preservation
        let trained_model = self.train_model_privately(base_model, &training_data)?;
        
        // 4. Generate model update (differential privacy)
        let model_update = self.generate_model_update(&base_model, &trained_model)?;
        
        // 5. Submit update to federated coordinator
        self.submit_model_update(model_id, model_update)?;
        
        Ok(TrainingResult {
            model_id: model_id.clone(),
            training_samples: training_data.len(),
            privacy_budget_used: self.get_privacy_budget_used(),
            contribution_score: self.calculate_contribution_score(&training_data),
            reward_estimate: self.estimate_rewards(&training_data),
        })
    }
    
    fn train_model_privately(&self, base_model: LocalModel, training_data: &LocalTrainingData) -> Result<LocalModel, TrainingError> {
        let mut model = base_model;
        
        // Apply differential privacy during training
        for epoch in 0..model.training_config.epochs {
            // 1. Add noise to gradients (differential privacy)
            let noisy_gradients = self.add_privacy_noise(&model.current_gradients)?;
            
            // 2. Update model with noisy gradients
            model.update_parameters(noisy_gradients)?;
            
            // 3. Validate privacy budget
            if self.privacy_manager.budget_exceeded() {
                break;
            }
        }
        
        Ok(model)
    }
    
    fn generate_model_update(&self, base_model: &LocalModel, trained_model: &LocalModel) -> Result<ModelUpdate, GenerationError> {
        // Calculate parameter differences
        let parameter_deltas = self.calculate_parameter_deltas(&base_model.parameters, &trained_model.parameters)?;
        
        // Apply additional privacy protection
        let private_deltas = self.apply_privacy_to_deltas(parameter_deltas)?;
        
        // Create verifiable update
        Ok(ModelUpdate {
            model_id: trained_model.id.clone(),
            parameter_updates: private_deltas,
            training_metadata: TrainingMetadata {
                samples_used: trained_model.training_samples,
                training_time: trained_model.training_duration,
                privacy_budget_used: trained_model.privacy_budget_used,
                model_accuracy: trained_model.validation_accuracy,
            },
            contribution_proof: self.generate_contribution_proof(trained_model)?,
            timestamp: current_timestamp(),
        })
    }
}

#[derive(Debug, Clone)]
pub struct ModelUpdate {
    pub model_id: ModelId,                     // Model ID
    pub parameter_updates: ParameterDeltas,    // Parameter updates
    pub training_metadata: TrainingMetadata,   // Training metadata
    pub contribution_proof: ContributionProof,  // Contribution proof
    pub timestamp: u64,                        // Timestamp
}

#[derive(Debug, Clone)]
pub struct TrainingMetadata {
    pub samples_used: usize,                   // Training samples used
    pub training_time: Duration,                 // Training duration
    pub privacy_budget_used: f64,               // Privacy budget consumed
    pub model_accuracy: f64,                    // Model accuracy
}

IPFS Integration & Model Storage

pub struct IPFSStorage {
    pub ipfs_client: IPFSClient,               // IPFS client
    pub model_cache: LRUCache<String, ModelData>, // Model cache
    pub storage_tracker: StorageTracker,        // Storage tracking
    pub integrity_checker: IntegrityChecker,    // Integrity verification
}

impl IPFSStorage {
    pub fn store_model(&mut self, model: &DistributedModel) -> Result<String, StorageError> {
        // 1. Serialize model with version
        let serialized_model = self.serialize_model_with_version(model)?;
        
        // 2. Calculate content hash
        let content_hash = self.calculate_content_hash(&serialized_model)?;
        
        // 3. Store on IPFS
        let ipfs_hash = self.ipfs_client.add_bytes(&serialized_model)?;
        
        // 4. Verify integrity
        self.verify_model_integrity(&ipfs_hash, &content_hash)?;
        
        // 5. Cache locally
        self.model_cache.put(ipfs_hash.clone(), ModelData {
            content: serialized_model,
            hash: content_hash,
            timestamp: current_timestamp(),
        });
        
        Ok(ipfs_hash)
    }
    
    pub fn retrieve_model(&mut self, ipfs_hash: &str) -> Result<DistributedModel, RetrievalError> {
        // 1. Check cache first
        if let Some(cached_data) = self.model_cache.get(ipfs_hash) {
            return self.deserialize_model(&cached_data.content);
        }
        
        // 2. Retrieve from IPFS
        let model_data = self.ipfs_client.cat_bytes(ipfs_hash)?;
        
        // 3. Verify integrity
        let calculated_hash = self.calculate_content_hash(&model_data)?;
        if !self.verify_hash_match(&calculated_hash, ipfs_hash) {
            return Err(RetrievalError::IntegrityViolation);
        }
        
        // 4. Cache retrieved model
        self.model_cache.put(ipfs_hash.to_string(), ModelData {
            content: model_data.clone(),
            hash: calculated_hash,
            timestamp: current_timestamp(),
        });
        
        // 5. Deserialize model
        self.deserialize_model(&model_data)
    }
    
    pub fn verify_model_provenance(&self, model: &DistributedModel) -> Result<ProvenanceVerification, VerificationError> {
        // 1. Verify IPFS hash matches content
        let stored_content = self.ipfs_client.cat_bytes(&model.ipfs_hash)?;
        let calculated_hash = self.calculate_content_hash(&stored_content)?;
        
        if !self.verify_ipfs_hash(&calculated_hash, &model.ipfs_hash) {
            return Ok(ProvenanceVerification {
                content_integrity: false,
                blockchain_reference: false,
                version_consistency: false,
                developer_signature: false,
            });
        }
        
        // 2. Verify blockchain reference
        let blockchain_valid = self.verify_blockchain_reference(&model.blockchain_ref)?;
        
        // 3. Verify version consistency
        let version_valid = self.verify_version_history(&model)?;
        
        // 4. Verify developer signature
        let signature_valid = self.verify_developer_signature(model)?;
        
        Ok(ProvenanceVerification {
            content_integrity: true,
            blockchain_reference: blockchain_valid,
            version_consistency: version_valid,
            developer_signature: signature_valid,
        })
    }
}

#[derive(Debug, Clone)]
pub struct ModelData {
    pub content: Vec<u8>,                      // Model content
    pub hash: String,                          // Content hash
    pub timestamp: u64,                        // Storage timestamp
}

#[derive(Debug, Clone)]
pub struct ProvenanceVerification {
    pub content_integrity: bool,               // Content integrity verified
    pub blockchain_reference: bool,             // Blockchain reference valid
    pub version_consistency: bool,             // Version history consistent
    pub developer_signature: bool,             // Developer signature valid
}

Blockchain Verification & Data Provenance

pub struct BlockchainVerifier {
    pub smart_contract: SmartContract,         // Verification contract
    pub provenance_tracker: ProvenanceTracker, // Provenance tracking
    pub reward_distributor: RewardDistributor, // Reward distribution
    pub audit_logger: AuditLogger,             // Audit logging
}

impl BlockchainVerifier {
    pub fn register_model(&mut self, model: &DistributedModel) -> Result<String, RegistrationError> {
        // 1. Create blockchain transaction for model registration
        let registration_tx = ModelRegistrationTransaction {
            model_id: model.id.clone(),
            developer: model.id.developer,
            model_type: model.model_type.clone(),
            ipfs_hash: model.ipfs_hash.clone(),
            version: model.id.version.clone(),
            reward_config: model.reward_config.clone(),
            timestamp: current_timestamp(),
            signature: self.sign_registration(model)?,
        };
        
        // 2. Submit transaction to blockchain
        let tx_hash = self.submit_transaction(registration_tx)?;
        
        // 3. Update provenance tracker
        self.provenance_tracker.register_model(&model.id, &tx_hash)?;
        
        // 4. Log registration for audit
        self.audit_logger.log_model_registration(&model.id, &tx_hash)?;
        
        Ok(tx_hash)
    }
    
    pub fn verify_data_provider(&self, provider: &DataProvider, contribution: &ModelContribution) -> Result<VerificationResult, VerificationError> {
        // 1. Verify provider identity
        let identity_valid = self.verify_provider_identity(provider)?;
        
        // 2. Verify contribution authenticity
        let contribution_valid = self.verify_contribution_authenticity(contribution)?;
        
        // 3. Check provider reputation
        let reputation_score = self.get_provider_reputation(&provider.address)?;
        
        // 4. Verify no double-spending of contributions
        let no_double_spend = self.check_contribution_uniqueness(contribution)?;
        
        Ok(VerificationResult {
            provider_valid: identity_valid,
            contribution_valid: contribution_valid,
            reputation_score,
            double_spend_check: no_double_spend,
            verification_timestamp: current_timestamp(),
        })
    }
    
    pub fn record_contribution(&mut self, contribution: &ModelContribution) -> Result<String, RecordError> {
        // 1. Create contribution record
        let record = ContributionRecord {
            contribution_id: self.generate_contribution_id(),
            model_id: contribution.model_id.clone(),
            provider: contribution.provider_address.clone(),
            training_samples: contribution.training_samples,
            privacy_budget_used: contribution.privacy_budget_used,
            contribution_proof: contribution.proof_hash.clone(),
            timestamp: current_timestamp(),
            reward_eligible: true,
        };
        
        // 2. Store on blockchain
        let record_hash = self.store_contribution_record(record)?;
        
        // 3. Update provider statistics
        self.update_provider_statistics(&contribution.provider_address, &record_hash)?;
        
        Ok(record_hash)
    }
}

#[derive(Debug, Clone)]
pub struct ModelContribution {
    pub model_id: ModelId,                     // Model ID
    pub provider_address: [u8; 32],            // Provider address
    pub training_samples: usize,               // Number of training samples
    pub privacy_budget_used: f64,               // Privacy budget consumed
    pub model_update: ModelUpdate,             // Model update
    pub proof_hash: String,                    // Contribution proof hash
    pub timestamp: u64,                        // Contribution timestamp
}

Predictive Execution Engine

pub struct PredictiveExecutor {
    pub timing_models: HashMap<TimingModelType, TimingModel>, // Timing models
    pub execution_predictor: ExecutionPredictor, // Execution prediction
    pub adaptive_scheduler: AdaptiveScheduler, // Adaptive scheduling
    pub performance_tracker: PerformanceTracker, // Performance tracking
}

#[derive(Debug, Clone, Hash, PartialEq, Eq)]
pub enum TimingModelType {
    GasPrice,                                // Gas price prediction
    NetworkCongestion,                        // Network congestion prediction
    BlockTime,                               // Block time prediction
    MempoolBehavior,                         // Mempool behavior prediction
    ValidatorActivity,                        // Validator activity prediction
}

impl PredictiveExecutor {
    pub fn execute_with_prediction(&mut self, tx: SignedTransaction, route: OptimizedRoute) -> Result<ExecutionResult, ExecutionError> {
        // 1. Predict optimal execution time
        let optimal_timing = self.predict_optimal_timing(&tx, &route)?;
        
        // 2. Schedule execution
        let scheduled_execution = self.schedule_execution(tx, route, optimal_timing)?;
        
        // 3. Monitor execution
        let execution_result = self.monitor_execution(&scheduled_execution)?;
        
        // 4. Update models based on results
        self.update_models(&execution_result)?;
        
        Ok(execution_result)
    }
    
    fn predict_optimal_timing(&self, tx: &SignedTransaction, route: &OptimizedRoute) -> Result<OptimalTiming, PredictionError> {
        // 1. Predict gas price trends
        let gas_price_prediction = self.predict_gas_price(tx)?;
        
        // 2. Predict network congestion
        let congestion_prediction = self.predict_network_congestion(tx)?;
        
        // 3. Predict block inclusion probability
        let inclusion_probability = self.predict_inclusion_probability(tx, route)?;
        
        // 4. Calculate optimal timing
        let optimal_timing = self.calculate_optimal_timing(
            gas_price_prediction,
            congestion_prediction,
            inclusion_probability,
        )?;
        
        Ok(optimal_timing)
    }
    
    fn predict_gas_price(&self, tx: &SignedTransaction) -> Result<GasPricePrediction, PredictionError> {
        let model = self.timing_models.get(&TimingModelType::GasPrice)
            .ok_or(PredictionError::ModelNotFound)?;
        
        // Extract features for gas price prediction
        let features = self.extract_gas_price_features(tx)?;
        
        // Predict gas prices for next 24 hours
        let predictions = model.predict_time_series(&features, 24)?;
        
        Ok(GasPricePrediction {
            current_price: self.get_current_gas_price()?,
            predictions,
            confidence: model.get_confidence(),
            trend: self.analyze_gas_price_trend(&predictions)?,
        })
    }
    
    fn predict_network_congestion(&self, tx: &SignedTransaction) -> Result<CongestionPrediction, PredictionError> {
        let model = self.timing_models.get(&TimingModelType::NetworkCongestion)
            .ok_or(PredictionError::ModelNotFound)?;
        
        // Extract features for congestion prediction
        let features = self.extract_congestion_features(tx)?;
        
        // Predict congestion levels
        let predictions = model.predict_time_series(&features, 12)?; // Next 12 hours
        
        Ok(CongestionPrediction {
            current_level: self.get_current_congestion_level()?,
            predictions,
            confidence: model.get_confidence(),
            peak_times: self.identify_peak_times(&predictions)?,
        })
    }
    
    fn calculate_optimal_timing(&self, gas_price: GasPricePrediction, congestion: CongestionPrediction, inclusion: InclusionProbability) -> Result<OptimalTiming, CalculationError> {
        let mut best_time = None;
        let mut best_score = 0.0;
        
        // Evaluate each time slot
        for hour in 0..24 {
            let gas_price_score = self.calculate_gas_price_score(&gas_price, hour);
            let congestion_score = self.calculate_congestion_score(&congestion, hour);
            let inclusion_score = self.calculate_inclusion_score(&inclusion, hour);
            
            let combined_score = (gas_price_score * 0.4) + (congestion_score * 0.3) + (inclusion_score * 0.3);
            
            if combined_score > best_score {
                best_score = combined_score;
                best_time = Some(hour);
            }
        }
        
        Ok(OptimalTiming {
            optimal_hour: best_time.ok_or(CalculationError::NoOptimalTime)?,
            expected_gas_price: gas_price.predictions[best_time.unwrap_or(0)],
            expected_congestion: congestion.predictions[best_time.unwrap_or(0)],
            expected_inclusion_probability: inclusion.probabilities[best_time.unwrap_or(0)],
            confidence: (gas_price.confidence + congestion.confidence) / 2.0,
            savings_estimate: self.calculate_savings_estimate(&gas_price, &congestion, best_time.unwrap_or(0))?,
        })
    }
}

Federated Learning Coordinator

pub struct LearningCoordinator {
    pub federated_learner: FederatedLearner,   // Federated learning
    pub model_aggregator: ModelAggregator,    // Model aggregation
    pub privacy_preserver: PrivacyPreserver,   // Privacy preservation
    pub participant_manager: ParticipantManager, // Participant management
}

impl LearningCoordinator {
    pub fn coordinate_learning_round(&mut self) -> Result<LearningRoundResult, LearningError> {
        // 1. Select participants
        let participants = self.participant_manager.select_participants()?;
        
        // 2. Distribute learning task
        let learning_task = self.create_learning_task()?;
        self.distribute_learning_task(&participants, &learning_task)?;
        
        // 3. Collect model updates
        let model_updates = self.collect_model_updates(&participants)?;
        
        // 4. Apply privacy preservation
        let private_updates = self.privacy_preserver.apply_privacy(model_updates)?;
        
        // 5. Aggregate models
        let aggregated_model = self.model_aggregator.aggregate_models(private_updates)?;
        
        // 6. Validate and deploy
        self.validate_and_deploy_model(aggregated_model)?;
        
        // 7. Update participant scores
        self.update_participant_scores(&participants)?;
        
        Ok(LearningRoundResult {
            participants_count: participants.len(),
            model_updates_received: model_updates.len(),
            aggregated_model_quality: self.evaluate_model_quality(&aggregated_model)?,
            privacy_loss: self.calculate_privacy_loss(&private_updates)?,
            learning_efficiency: self.calculate_learning_efficiency(&participants, &model_updates)?,
        })
    }
    
    fn create_learning_task(&self) -> Result<LearningTask, LearningError> {
        Ok(LearningTask {
            task_id: self.generate_task_id(),
            model_type: ModelType::Routing,
            training_data_requirements: TrainingDataRequirements {
                min_samples: 1000,
                data_types: vec![DataType::Transaction, DataType::Network, DataType::User],
                time_range: Duration::from_secs(86400 * 7), // 7 days
                quality_threshold: 0.8,
            },
            privacy_requirements: PrivacyRequirements {
                differential_privacy: true,
                epsilon: 1.0,
                delta: 1e-5,
                secure_aggregation: true,
            },
            optimization_objectives: vec![
                OptimizationObjective::MinimizeCost,
                OptimizationObjective::MinimizeLatency,
                OptimizationObjective::MaximizeSuccessRate,
            ],
            deadline: current_timestamp() + 3600, // 1 hour from now
        })
    }
}

pub struct FederatedLearner {
    pub local_models: HashMap<ModelType, LocalModel>, // Local models
    pub training_data: LocalTrainingData,      // Local training data
    pub privacy_config: PrivacyConfig,         // Privacy configuration
}

impl FederatedLearner {
    pub fn train_local_model(&mut self, task: &LearningTask) -> Result<LocalModelUpdate, TrainingError> {
        // 1. Prepare local training data
        let training_data = self.prepare_training_data(&task.training_data_requirements)?;
        
        // 2. Train local model
        let local_model = self.train_model_on_data(&task.model_type, &training_data)?;
        
        // 3. Apply privacy preservation
        let private_model = self.apply_privacy_to_model(local_model, &task.privacy_requirements)?;
        
        // 4. Create model update
        let model_update = LocalModelUpdate {
            participant_id: self.get_participant_id(),
            model_type: task.model_type.clone(),
            model_parameters: private_model.parameters,
            training_metadata: TrainingMetadata {
                samples_used: training_data.len(),
                training_time: private_model.training_time,
                model_quality: private_model.quality_score,
                privacy_budget_used: private_model.privacy_budget_used,
            },
        };
        
        Ok(model_update)
    }
    
    fn apply_privacy_to_model(&self, model: LocalModel, requirements: &PrivacyRequirements) -> Result<PrivateModel, PrivacyError> {
        let mut private_model = PrivateModel::from(model);
        
        // 1. Apply differential privacy
        if requirements.differential_privacy {
            private_model = self.apply_differential_privacy(private_model, requirements.epsilon, requirements.delta)?;
        }
        
        // 2. Apply secure aggregation
        if requirements.secure_aggregation {
            private_model = self.apply_secure_aggregation(private_model)?;
        }
        
        Ok(private_model)
    }
    
    fn apply_differential_privacy(&self, model: LocalModel, epsilon: f64, delta: f64) -> Result<PrivateModel, PrivacyError> {
        // Add noise to model parameters
        let noisy_parameters = self.add_noise_to_parameters(&model.parameters, epsilon, delta)?;
        
        Ok(PrivateModel {
            parameters: noisy_parameters,
            privacy_budget_used: epsilon,
            noise_scale: self.calculate_noise_scale(epsilon, delta),
            quality_score: self.calculate_noisy_quality_score(&model.parameters, &noisy_parameters),
            training_time: model.training_time,
        })
    }
    
    fn add_noise_to_parameters(&self, parameters: &ModelParameters, epsilon: f64, delta: f64) -> Result<ModelParameters, PrivacyError> {
        let mut noisy_parameters = parameters.clone();
        
        for (key, value) in &mut noisy_parameters.parameters {
            let noise = self.generate_noise(value, epsilon, delta)?;
            *value += noise;
        }
        
        Ok(noisy_parameters)
    }
    
    fn generate_noise(&self, value: &ParameterValue, epsilon: f64, delta: f64) -> Result<f64, PrivacyError> {
        match value {
            ParameterValue::Float(f) => {
                let scale = 2.0 * f.abs() / epsilon;
                let noise = self.sample_laplace_noise(scale)?;
                Ok(noise)
            },
            ParameterValue::Integer(i) => {
                let scale = 2.0 * (*i as f64).abs() / epsilon;
                let noise = self.sample_laplace_noise(scale)?;
                Ok(noise.round())
            },
            ParameterValue::Vector(v) => {
                let mut total_noise = 0.0;
                for &val in v {
                    let scale = 2.0 * val.abs() / epsilon;
                    total_noise += self.sample_laplace_noise(scale)?;
                }
                Ok(total_noise / v.len() as f64)
            },
        }
    }
    
    fn sample_laplace_noise(&self, scale: f64) -> Result<f64, PrivacyError> {
        use rand::distributions::{Laplace, Distribution};
        let laplace = Laplace::new(0.0, scale);
        Ok(laplace.sample(&mut rand::thread_rng()))
    }
}

Reward Distribution & Incentive System

pub struct RewardManager {
    pub reward_calculator: RewardCalculator,   // Reward calculation
    pub token_distributor: TokenDistributor,   // Token distribution
    pub reputation_system: ReputationSystem,   // Reputation tracking
    pub incentive_optimizer: IncentiveOptimizer, // Incentive optimization
}

impl RewardManager {
    pub fn calculate_rewards(&self, contribution: &ModelContribution, model_performance: &ModelMetrics) -> Result<RewardBreakdown, CalculationError> {
        // 1. Base reward for data contribution
        let base_reward = self.calculate_base_data_reward(contribution)?;
        
        // 2. Quality bonus based on model improvement
        let quality_bonus = self.calculate_quality_bonus(model_performance)?;
        
        // 3. Privacy preservation bonus
        let privacy_bonus = self.calculate_privacy_bonus(contribution.privacy_budget_used)?;
        
        // 4. Reputation multiplier
        let reputation_multiplier = self.get_reputation_multiplier(&contribution.provider_address)?;
        
        // 5. Early contributor bonus
        let early_contributor_bonus = self.calculate_early_contributor_bonus(&contribution.model_id)?;
        
        let total_reward = (base_reward + quality_bonus + privacy_bonus + early_contributor_bonus) * reputation_multiplier;
        
        Ok(RewardBreakdown {
            base_data_reward: base_reward,
            quality_improvement_bonus: quality_bonus,
            privacy_preservation_bonus: privacy_bonus,
            reputation_multiplier,
            early_contributor_bonus,
            total_reward,
            reward_token: self.get_reward_token(),
            vesting_schedule: self.calculate_vesting_schedule(total_reward),
        })
    }
    
    pub fn distribute_rewards(&mut self, rewards: &[RewardDistribution]) -> Result<Vec<TransactionHash>, DistributionError> {
        let mut distribution_txs = Vec::new();
        
        for reward in rewards {
            // 1. Create reward transaction
            let reward_tx = RewardTransaction {
                recipient: reward.recipient_address,
                amount: reward.amount,
                token_type: reward.token_type.clone(),
                vesting_schedule: reward.vesting_schedule.clone(),
                contribution_reference: reward.contribution_hash.clone(),
                timestamp: current_timestamp(),
                signature: self.sign_reward_transaction(reward)?,
            };
            
            // 2. Submit to blockchain
            let tx_hash = self.submit_reward_transaction(reward_tx)?;
            
            // 3. Update reputation
            self.reputation_system.update_reputation(&reward.recipient_address, reward.amount)?;
            
            // 4. Track distribution
            distribution_txs.push(tx_hash);
        }
        
        Ok(distribution_txs)
    }
    
    fn calculate_base_data_reward(&self, contribution: &ModelContribution) -> Result<u128, CalculationError> {
        // Base reward per training sample
        let per_sample_rate = self.get_current_data_rate();
        
        // Adjust for data quality and uniqueness
        let quality_multiplier = self.assess_data_quality(contribution)?;
        let uniqueness_multiplier = self.assess_data_uniqueness(contribution)?;
        
        let base_reward = (contribution.training_samples as u128) 
            * per_sample_rate 
            * quality_multiplier 
            * uniqueness_multiplier;
        
        Ok(base_reward)
    }
    
    fn calculate_quality_bonus(&self, model_performance: &ModelMetrics) -> Result<u128, CalculationError> {
        // Bonus based on model improvement metrics
        let accuracy_improvement = model_performance.accuracy_improvement;
        let efficiency_improvement = model_performance.efficiency_improvement;
        let generalization_score = model_performance.generalization_score;
        
        let quality_score = (accuracy_improvement + efficiency_improvement + generalization_score) / 3.0;
        let bonus_rate = self.get_quality_bonus_rate();
        
        Ok((quality_score * bonus_rate as f64) as u128)
    }
}

#[derive(Debug, Clone)]
pub struct RewardBreakdown {
    pub base_data_reward: u128,               // Base reward for data
    pub quality_improvement_bonus: u128,       // Bonus for model improvement
    pub privacy_preservation_bonus: u128,      // Bonus for privacy preservation
    pub reputation_multiplier: f64,            // Reputation multiplier
    pub early_contributor_bonus: u128,        // Early contributor bonus
    pub total_reward: u128,                   // Total reward amount
    pub reward_token: String,                  // Reward token type
    pub vesting_schedule: VestingSchedule,     // Vesting schedule
}

#[derive(Debug, Clone)]
pub struct RewardDistribution {
    pub recipient_address: [u8; 32],          // Recipient address
    pub amount: u128,                          // Reward amount
    pub token_type: String,                    // Token type
    pub contribution_hash: String,             // Reference contribution
    pub vesting_schedule: VestingSchedule,     // Vesting schedule
    pub distribution_reason: DistributionReason, // Reason for reward
}

#[derive(Debug, Clone)]
pub enum DistributionReason {
    DataContribution,                          // Data provided
    ModelTraining,                             // Model training
    QualityImprovement,                        // Quality improvements
    PrivacyPreservation,                       // Privacy preservation
    EarlyAdoption,                            // Early contribution
    ReputationBonus,                          // Reputation bonus
}

Developer Model Merging & Version Management

pub struct ModelVersionManager {
    pub version_control: VersionControl,        // Version control
    pub merge_engine: MergeEngine,              // Model merging
    pub quality_assurance: QualityAssurance,   // Quality assurance
    pub deployment_manager: DeploymentManager, // Deployment management
}

impl ModelVersionManager {
    pub fn merge_model_updates(&mut self, base_model: &DistributedModel, updates: &[ModelUpdate]) -> Result<UpdatedModel, MergeError> {
        // 1. Validate all updates
        self.validate_model_updates(base_model, updates)?;
        
        // 2. Sort updates by quality and contribution
        let sorted_updates = self.sort_updates_by_quality(updates)?;
        
        // 3. Apply federated averaging or other merge strategy
        let merged_parameters = self.merge_parameters(&base_model.parameters, &sorted_updates)?;
        
        // 4. Validate merged model
        let merged_model = self.create_merged_model(base_model, merged_parameters)?;
        self.validate_merged_model(&merged_model)?;
        
        // 5. Performance testing
        let performance_metrics = self.test_model_performance(&merged_model)?;
        
        Ok(UpdatedModel {
            model: merged_model,
            performance_metrics,
            merge_statistics: self.calculate_merge_statistics(&sorted_updates),
            quality_score: self.calculate_model_quality(&performance_metrics),
            version_increment: self.calculate_version_increment(base_model, &merged_model),
        })
    }
    
    pub fn deploy_updated_model(&mut self, updated_model: &UpdatedModel) -> Result<DeploymentResult, DeploymentError> {
        // 1. Create new version
        let new_version = self.create_new_version(&updated_model.model)?;
        
        // 2. Store on IPFS
        let ipfs_hash = self.store_model_version(&updated_model.model)?;
        
        // 3. Update blockchain reference
        let blockchain_ref = self.update_blockchain_reference(&new_version, &ipfs_hash)?;
        
        // 4. Update model registry
        self.update_model_registry(&updated_model.model.id, &new_version)?;
        
        // 5. Notify distribution network
        self.notify_model_update(&updated_model.model.id, &new_version)?;
        
        Ok(DeploymentResult {
            version: new_version,
            ipfs_hash,
            blockchain_ref,
            deployment_timestamp: current_timestamp(),
            rollback_plan: self.create_rollback_plan(&updated_model.model)?,
        })
    }
    
    fn merge_parameters(&self, base_params: &ModelParameters, updates: &[ModelUpdate]) -> Result<ModelParameters, MergeError> {
        let mut merged_params = base_params.clone();
        
        // Federated averaging with quality weighting
        let total_weight: f64 = updates.iter().map(|u| u.training_metadata.model_accuracy).sum();
        
        for (param_name, base_value) in &mut merged_params.parameters {
            let mut weighted_sum = 0.0;
            let mut weight_total = 0.0;
            
            for update in updates {
                if let Some(update_value) = update.parameter_updates.parameters.get(param_name) {
                    let weight = update.training_metadata.model_accuracy;
                    weighted_sum += *update_value * weight;
                    weight_total += weight;
                }
            }
            
            if weight_total > 0.0 {
                // Weighted average with base model preservation
                let base_weight = 0.1; // Preserve 10% of base model
                let update_weight = 0.9; // 90% from updates
                *param_value = (*base_value * base_weight) + (weighted_sum / weight_total * update_weight);
            }
        }
        
        Ok(merged_params)
    }
}

#[derive(Debug, Clone)]
pub struct UpdatedModel {
    pub model: DistributedModel,               // Updated model
    pub performance_metrics: ModelMetrics,      // Performance metrics
    pub merge_statistics: MergeStatistics,     // Merge statistics
    pub quality_score: f64,                    // Quality score
    pub version_increment: VersionIncrement,   // Version increment
}

#[derive(Debug, Clone)]
pub struct MergeStatistics {
    pub total_updates: usize,                  // Total updates merged
    pub average_quality: f64,                  // Average update quality
    pub diversity_score: f64,                  // Data diversity score
    pub privacy_preservation: f64,             // Privacy preservation score
    pub merge_efficiency: f64,                 // Merge efficiency
}

Performance Monitoring

AITL Performance Metrics

pub struct AITLPerformanceMetrics {
    pub model_performance: ModelPerformanceMetrics, // Model performance
    pub routing_performance: RoutingPerformanceMetrics, // Routing performance
    pub learning_performance: LearningPerformanceMetrics, // Learning performance
    pub privacy_metrics: PrivacyMetrics,          // Privacy metrics
    pub user_satisfaction: UserSatisfactionMetrics, // User satisfaction
}

impl AITLPerformanceMetrics {
    pub fn calculate_overall_performance(&self) -> PerformanceScore {
        let model_weight = 0.3;
        let routing_weight = 0.3;
        let learning_weight = 0.2;
        let privacy_weight = 0.1;
        let satisfaction_weight = 0.1;
        
        let model_score = self.model_performance.calculate_score();
        let routing_score = self.routing_performance.calculate_score();
        let learning_score = self.learning_performance.calculate_score();
        let privacy_score = self.privacy_metrics.calculate_score();
        let satisfaction_score = self.user_satisfaction.calculate_score();
        
        let overall_score = (model_score * model_weight) +
                           (routing_score * routing_weight) +
                           (learning_score * learning_weight) +
                           (privacy_score * privacy_weight) +
                           (satisfaction_score * satisfaction_weight);
        
        PerformanceScore {
            overall: overall_score,
            components: PerformanceComponents {
                model: model_score,
                routing: routing_score,
                learning: learning_score,
                privacy: privacy_score,
                satisfaction: satisfaction_score,
            },
            trends: self.calculate_performance_trends(),
            recommendations: self.generate_performance_recommendations(),
        }
    }
}

Configuration

AITL Configuration

pub struct AITLConfig {
    pub model_config: ModelConfig,             // Model configuration
    pub learning_config: LearningConfig,       // Learning configuration
    pub privacy_config: PrivacyConfig,         // Privacy configuration
    pub performance_config: PerformanceConfig,  // Performance configuration
}

impl Default for AITLConfig {
    fn default() -> Self {
        Self {
            model_config: ModelConfig::default(),
            learning_config: LearningConfig::default(),
            privacy_config: PrivacyConfig::default(),
            performance_config: PerformanceConfig::default(),

#### Phase 1: Foundation (Q1 2026)
- IPFS integration implementation
- Blockchain verification smart contracts
- Basic model distribution framework
- Privacy preservation infrastructure

#### Phase 2: Federated Learning (Q2 2026)
- Local training engine implementation
- Federated coordination system
- Privacy-preserving model aggregation
- Contribution tracking system

#### Phase 3: Reward System (Q3 2026)
- Token distribution mechanism
- Reputation system implementation
- Quality assessment framework
- Incentive optimization algorithms

#### Phase 4: Developer Tools (Q4 2026)
- Model merging and versioning
- Deployment automation
- Performance monitoring
- Developer SDK and APIs

## Technical Requirements

### System Requirements
- **Storage**: IPFS node with 100GB+ capacity
- **Network**: 1Gbps+ for model distribution
- **Compute**: GPU support for local training (optional)
- **Blockchain**: Savitri Network node access

### Privacy Requirements
- **Differential Privacy**: ε ≤ 1.0, δ ≤ 1e-5
- **Local Training**: No raw data leaves device
- **Secure Aggregation**: Encrypted model updates
- **Audit Trail**: Complete contribution tracking

### Performance Targets

    #### NFT Contracts for AI Ownership

    **Savitri721 (SNT1) - AI Model Ownership**
    ```rust
    // src/contracts/standards/savitri721.rs
    pub struct AIModelNFT;

    impl AIModelNFT {
        /// Mint a new AI model NFT representing ownership
        pub fn mint_model_nft(
            storage: &mut Storage,
            contract_address: &[u8; 32],
            owner: &[u8; 32],
            model_id: &ModelId,
            model_metadata: &ModelMetadata,
            gas_meter: &mut GasMeter,
        ) -> Result<u64, ContractError> {
            // 1. Generate unique token ID
            let token_id = self.generate_model_token_id(model_id)?;
            
            // 2. Store model metadata in token URI
            let token_uri = self.serialize_model_metadata(model_metadata)?;
            self.set_token_uri(storage, contract_address, token_id, &token_uri)?;
            
            // 3. Mint NFT to developer
            self._mint(storage, contract_address, owner, token_id)?;
            
            // 4. Emit ModelMinted event
            self.emit_model_minted_event(storage, contract_address, owner, token_id, model_id)?;
            
            Ok(token_id)
        }
        
        /// Transfer model ownership with update rights
        pub fn transfer_model_ownership(
            storage: &mut Storage,
            contract_address: &[u8; 32],
            from: &[u8; 32],
            to: &[u8; 32],
            token_id: u64,
            gas_meter: &mut GasMeter,
        ) -> Result<(), ContractError> {
            // 1. Verify ownership
            let current_owner = self.owner_of(storage, contract_address, token_id)?;
            if current_owner != from {
                return Err(ContractError::Unauthorized);
            }
            
            // 2. Transfer NFT
            self._transfer(storage, contract_address, from, to, token_id)?;
            
            // 3. Update model registry
            self.update_model_registry(storage, contract_address, token_id, to)?;
            
            Ok(())
        }
    }

    #[derive(Debug, Clone)]
    pub struct ModelMetadata {
        pub model_id: ModelId,                     // Model identifier
        pub developer: [u8; 32],                    // Developer address
        pub model_type: ModelType,                  // Model type
        pub version: String,                        // Model version
        pub ipfs_hash: String,                      // IPFS content hash
        pub created_at: u64,                        // Creation timestamp
        pub training_contributors: Vec<[u8; 32]>,   // Training contributors
        pub performance_metrics: ModelMetrics,     // Performance metrics
    }

Token Contracts for Reward Distribution

Savitri20 (SAVITRI-20) - AITL Reward Token

// src/contracts/standards/savitri20.rs
pub struct AITLRewardToken;

impl AITLRewardToken {
    /// Mint reward tokens for data contributors
    pub fn mint_rewards(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        recipient: &[u8; 32],
        amount: u128,
        contribution_id: &str,
        gas_meter: &mut GasMeter,
    ) -> Result<(), ContractError> {
        // 1. Verify minter permissions (AITL system only)
        if !self.is_authorized_minter(storage, contract_address)? {
            return Err(ContractError::Unauthorized);
        }
        
        // 2. Mint tokens
        self._mint(storage, contract_address, recipient, amount)?;
        
        // 3. Record reward distribution
        self.record_reward_distribution(storage, contract_address, recipient, amount, contribution_id)?;
        
        // 4. Emit RewardMinted event
        self.emit_reward_minted_event(storage, contract_address, recipient, amount, contribution_id)?;
        
        Ok(())
    }
    
    /// Create vesting schedule for long-term incentives
    pub fn create_vesting_schedule(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        beneficiary: &[u8; 32],
        total_amount: u128,
        vesting_period: u64,
        cliff_period: u64,
        gas_meter: &mut GasMeter,
    ) -> Result<u64, ContractError> {
        let vesting_id = self.generate_vesting_id();
        
        let vesting_schedule = VestingSchedule {
            beneficiary,
            total_amount,
            vested_amount: 0,
            vesting_period,
            cliff_period,
            start_time: current_timestamp(),
            last_claim_time: 0,
        };
        
        self.store_vesting_schedule(storage, contract_address, vesting_id, &vesting_schedule)?;
        
        Ok(vesting_id)
    }
}

#[derive(Debug, Clone)]
pub struct VestingSchedule {
    pub beneficiary: [u8; 32],                // Beneficiary address
    pub total_amount: u128,                   // Total tokens to vest
    pub vested_amount: u128,                  // Already vested amount
    pub vesting_period: u64,                   // Total vesting period
    pub cliff_period: u64,                    // Cliff period
    pub start_time: u64,                      // Vesting start time
    pub last_claim_time: u64,                  // Last claim timestamp
}

AI Orchestration Contract

AITL Orchestration - Model Update Coordination

// src/contracts/aitl_orchestration.rs
pub struct AITLOrchestration;

impl AITLOrchestration {
    /// Register new AI model for federated learning
    pub fn register_model(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        developer: &[u8; 32],
        model_config: &ModelConfig,
        reward_config: &RewardConfig,
        gas_meter: &mut GasMeter,
    ) -> Result<ModelId, ContractError> {
        // 1. Validate developer permissions
        if !self.is_authorized_developer(storage, contract_address, developer)? {
            return Err(ContractError::Unauthorized);
        }
        
        // 2. Generate model ID
        let model_id = self.generate_model_id(developer, model_config)?;
        
        // 3. Store model configuration
        self.store_model_config(storage, contract_address, &model_id, model_config)?;
        
        // 4. Store reward configuration
        self.store_reward_config(storage, contract_address, &model_id, reward_config)?;
        
        // 5. Initialize contribution tracking
        self.initialize_contribution_tracking(storage, contract_address, &model_id)?;
        
        // 6. Emit ModelRegistered event
        self.emit_model_registered_event(storage, contract_address, developer, &model_id)?;
        
        Ok(model_id)
    }
    
    /// Submit federated learning contribution
    pub fn submit_contribution(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        contributor: &[u8; 32],
        model_id: &ModelId,
        model_update: &ModelUpdate,
        proof: &ContributionProof,
        gas_meter: &mut GasMeter,
    ) -> Result<u64, ContractError> {
        // 1. Verify model exists and is active
        if !self.is_model_active(storage, contract_address, model_id)? {
            return Err(ContractError::ModelNotFound);
        }
        
        // 2. Verify contribution proof
        if !self.verify_contribution_proof(storage, contract_address, model_id, proof)? {
            return Err(ContractError::InvalidProof);
        }
        
        // 3. Check contribution uniqueness
        if self.is_duplicate_contribution(storage, contract_address, proof)? {
            return Err(ContractError::DuplicateContribution);
        }
        
        // 4. Store contribution
        let contribution_id = self.store_contribution(
            storage, contract_address, contributor, model_id, model_update, proof
        )?;
        
        // 5. Update contributor statistics
        self.update_contributor_stats(storage, contract_address, contributor, &model_id)?;
        
        // 6. Emit ContributionSubmitted event
        self.emit_contribution_submitted_event(storage, contract_address, contributor, &model_id, contribution_id)?;
        
        Ok(contribution_id)
    }
    
    /// Trigger model merging and version update
    pub fn trigger_model_merge(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        model_id: &ModelId,
        merge_config: &MergeConfig,
        gas_meter: &mut GasMeter,
    ) -> Result<MergeRoundId, ContractError> {
        // 1. Verify merge conditions met
        if !self.are_merge_conditions_met(storage, contract_address, model_id, merge_config)? {
            return Err(ContractError::MergeConditionsNotMet);
        }
        
        // 2. Collect pending contributions
        let contributions = self.collect_pending_contributions(storage, contract_address, model_id)?;
        
        // 3. Create merge round
        let merge_round_id = self.create_merge_round(storage, contract_address, model_id, &contributions)?;
        
        // 4. Lock contributions for merging
        self.lock_contributions_for_merge(storage, contract_address, &contributions)?;
        
        // 5. Emit MergeTriggered event
        self.emit_merge_triggered_event(storage, contract_address, model_id, merge_round_id, contributions.len())?;
        
        Ok(merge_round_id)
    }
    
    /// Complete model merge and deploy new version
    pub fn complete_model_merge(
        storage: &mut Storage,
        contract_address: &[u8; 32],
        merge_round_id: MergeRoundId,
        merged_model: &MergedModel,
        gas_meter: &mut GasMeter,
    ) -> Result<ModelVersion, ContractError> {
        // 1. Verify merge round exists and is pending
        let merge_round = self.get_merge_round(storage, contract_address, merge_round_id)?;
        if merge_round.status != MergeStatus::Pending {
            return Err(ContractError::InvalidMergeStatus);
        }
        
        // 2. Verify merged model integrity
        if !self.verify_merged_model_integrity(storage, contract_address, merged_model)? {
            return Err(ContractError::InvalidMergedModel);
        }
        
        // 3. Calculate rewards for contributors
        let reward_distribution = self.calculate_contributor_rewards(storage, contract_address, &merge_round)?;
        
        // 4. Distribute rewards
        self.distribute_merge_rewards(storage, contract_address, &reward_distribution)?;
        
        // 5. Update model version
        let new_version = self.update_model_version(storage, contract_address, merged_model)?;
        
        // 6. Update merge round status
        self.update_merge_round_status(storage, contract_address, merge_round_id, MergeStatus::Completed)?;
        
        // 7. Emit MergeCompleted event
        self.emit_merge_completed_event(storage, contract_address, merge_round_id, &new_version)?;
        
        Ok(new_version)
    }
}

#[derive(Debug, Clone)]
pub struct ModelConfig {
    pub model_type: ModelType,                  // Model type
    pub architecture: ModelArchitecture,        // Model architecture
    pub training_requirements: TrainingRequirements, // Training requirements
    pub privacy_requirements: PrivacyRequirements, // Privacy requirements
    pub quality_threshold: f64,                  // Minimum quality threshold
}

#[derive(Debug, Clone)]
pub struct RewardConfig {
    pub total_reward_pool: u128,                // Total reward pool
    pub reward_per_sample: u128,               // Reward per training sample
    pub quality_bonus_rate: f64,                 // Quality bonus multiplier
    pub privacy_bonus_rate: f64,                // Privacy preservation bonus
    pub early_contributor_bonus: u128,          // Early contributor bonus
    pub vesting_period: u64,                    // Token vesting period
}

Technology Choice Rationale

Why Smart Contract Integration

Problem Statement: Federated AI learning requires verifiable ownership, transparent reward distribution, and tamper-proof coordination of model updates across multiple participants.

Chosen Solution: Native Savitri smart contracts with NFT ownership, fungible reward tokens, and orchestration contracts.

Rationale:

• Verifiable Ownership: NFTs provide immutable proof of AI model ownership
• Transparent Rewards: Token contracts ensure fair and auditable reward distribution
• Coordination Logic: Orchestration contracts automate model merging with tamper-proof logic
• Privacy Preservation: On-chain verification without exposing training data
• Composability: Leverages existing Savitri contract standards and infrastructure

Expected Results:

• Immutable ownership records for AI models and updates
• Transparent and auditable reward distribution
• Automated coordination of federated learning rounds
• Reduced trust requirements through smart contract automation
• Integration with existing Savitri DeFi and governance ecosystems

Why NFT-based Model Ownership

Problem Statement: AI models need ownership representation that can track provenance, enable transfers, and maintain update rights throughout the model lifecycle.

Chosen Solution: SAVITRI-721 NFTs with embedded model metadata and transfer restrictions.

Rationale:

• Provenance Tracking: NFTs provide complete ownership history
• Metadata Storage: Model specifications and performance metrics embedded
• Transfer Controls: Smart contract rules for ownership transfers
• Update Rights: NFT ownership grants model update coordination rights
• Market Integration: Compatible with existing NFT marketplaces

Expected Results:

• Tamper-proof model ownership records
• Complete provenance tracking for AI models
• Controlled transfer of model ownership and update rights
• Integration with broader NFT ecosystem
• Verifiable model authenticity and history

Why Token-based Reward System

Problem Statement: Contributors need fair, transparent, and timely compensation for their data and compute contributions to federated learning.

Chosen Solution: SAVITRI-20 tokens with vesting schedules and contribution-based distribution.

Rationale:

• Liquidity: Fungible tokens provide easy reward exchange
• Vesting: Long-term incentives through vesting schedules
• Transparency: On-chain reward distribution visible to all
• Programmability: Complex reward logic through smart contracts
• DeFi Integration: Compatible with existing DeFi protocols

Expected Results:

• Fair and transparent reward distribution
• Long-term contributor retention through vesting
• Immediate reward liquidity through token markets
• Complex reward calculations automated on-chain
• Integration with broader DeFi ecosystem

Codebase Coherence Analysis

✅ Coherent Components:

• BaseContract: All contracts extend BaseContract (slot 0-99 reserved)
• Storage Layout: Proper slot allocation following Savitri patterns
• Event System: Uses native event emission for transparency
• Gas Metering: Proper gas calculation and metering
• Runtime Integration: Compatible with Savitri contract runtime

✅ Standard Compliance:

• SAVITRI-721: NFT standard for model ownership
• SAVITRI-20: Fungible token standard for rewards
• Storage Patterns: Keccak256-based slot calculations
• Error Handling: Consistent error types and handling

✅ Architecture Alignment:

• Modular Design: Separate contracts for different functions
• Upgradeability: Built-in upgrade mechanisms
• Governance Integration: Compatible with Savitri governance
• Cross-Contract Calls: Proper contract-to-contract interactions

The proposed smart contract integration is fully coherent with the existing Savitri codebase and leverages native contract standards for maximum compatibility and security.