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Federated Learning Governance

Overview​

The Savitri Network Federated Learning (FL) governance system enables decentralized AI model training and validation through blockchain-based coordination. The system provides a secure framework for model distribution, federated training, and reward distribution while maintaining privacy and data sovereignty.

FL Architecture​

Core Components​

pub struct FLGovernance {
    pub policy_manager: FLPolicyManager,      // Policy management
    pub model_registry: ModelRegistry,        // Model registry
    pub aggregator_whitelist: Whitelist,      // Aggregator whitelist
    pub training_coordinator: TrainingCoordinator, // Training coordination
    pub reward_distributor: RewardDistributor, // Reward distribution
}

FL Policy Management​

pub struct FLPolicy {
    pub fee_treasury_bps: u16,                  // Fee percentage to treasury
    pub max_models: u32,                       // Maximum active models
    pub min_participants: u32,                  // Minimum participants
    pub whitelist_aggregators: Vec<Vec<u8>>,   // Approved aggregators
    pub min_reputation: f64,                   // Minimum reputation score
    pub model_approval_required: bool,        // Require model approval
    pub privacy_requirements: PrivacyRequirements, // Privacy requirements
    pub training_parameters: TrainingParameters, // Training parameters
}

pub struct PrivacyRequirements {
    pub differential_privacy: bool,            // Differential privacy
    pub min_noise_level: f64,                 // Minimum noise level
    pub max_data_points_per_participant: u32,  // Max data points per participant
    pub data_encryption: bool,                 // Data encryption required
    pub audit_trail: bool,                     // Audit trail required
}

pub struct TrainingParameters {
    pub max_rounds: u32,                       // Maximum training rounds
    pub min_accuracy: f64,                    // Minimum accuracy
    pub convergence_threshold: f64,            // Convergence threshold
    pub timeout_per_round: Duration,           // Timeout per round
    pub communication_budget: u64,              // Communication budget
}

Model Registry​

Model Registration​

pub struct ModelRegistration {
    pub model_id: Vec<u8>,                     // Model identifier
    pub creator: Vec<u8>,                      // Creator address
    pub model_type: ModelType,                 // Model type
    pub model_version: String,                  // Model version
    pub model_hash: Vec<u8>,                    // Model content hash
    pub metadata: ModelMetadata,               // Model metadata
    pub training_requirements: TrainingRequirements, // Training requirements
    pub approval_status: ApprovalStatus,       // Approval status
    pub registration_timestamp: u64,            // Registration timestamp
}

pub struct ModelMetadata {
    pub name: String,                          // Model name
    pub description: String,                    // Model description
    pub input_shape: Vec<usize>,               // Input shape
    pub output_shape: Vec<usize>,              // Output shape
    pub parameters: ModelParameters,           // Model parameters
    pub performance_metrics: PerformanceMetrics, // Performance metrics
    pub use_cases: Vec<String>,                 // Use cases
}

pub struct ModelParameters {
    pub total_parameters: u64,                 // Total parameters
    pub trainable_parameters: u64,              // Trainable parameters
    pub model_size_mb: f64,                    // Model size in MB
    pub inference_time_ms: f64,                 // Inference time
    pub training_time_per_epoch: f64,          // Training time per epoch
    pub memory_requirement_mb: f64,             // Memory requirement
}

Model Approval Process​

pub struct ModelApproval {
    pub model_id: Vec<u8>,                     // Model identifier
    pub approval_votes: u128,                   // Approval votes
    pub rejection_votes: u128,                  // Rejection votes
    pub total_voting_power: u128,               // Total voting power
    pub approval_threshold: f64,                // Approval threshold
    pub review_period: Duration,                // Review period
    pub technical_review: TechnicalReview,      // Technical review
    pub security_review: SecurityReview,        // Security review
}

pub struct TechnicalReview {
    pub code_quality: QualityScore,            // Code quality score
    pub performance_score: PerformanceScore,    // Performance score
    pub architecture_review: ArchitectureReview, // Architecture review
    pub compliance_check: ComplianceCheck,       // Compliance check
    pub reviewer_comments: Vec<String>,          // Reviewer comments
}

pub struct SecurityReview {
    pub vulnerability_scan: VulnerabilityScan,   // Vulnerability scan
    pub privacy_analysis: PrivacyAnalysis,      // Privacy analysis
    pub data_protection: DataProtection,        // Data protection
    pub access_control: AccessControl,          // Access control
    pub security_rating: SecurityRating,        // Security rating
}

Training Coordination​

Training Round Management​

pub struct TrainingRound {
    pub round_id: u64,                         // Round identifier
    pub model_id: Vec<u8],                     // Model identifier
    pub aggregator: Vec<u8>,                    // Aggregator address
    pub participants: Vec<Participant>,         // Participants
    pub training_parameters: TrainingParameters, // Training parameters
    pub start_time: u64,                       // Start time
    pub end_time: u64,                         // End time
    pub status: RoundStatus,                   // Round status
    pub results: Option<TrainingResults>,      // Training results
}

pub struct Participant {
    pub participant_id: Vec<u8>,               // Participant identifier
    pub reputation_score: f64,                 // Reputation score
    pub data_contribution: DataContribution,   // Data contribution
    pub training_power: f64,                  // Training power
    pub availability: Availability,            // Availability
    pub privacy_level: PrivacyLevel,           // Privacy level
}

pub struct DataContribution {
    pub data_points: u32,                     // Data points contributed
    pub data_quality: f64,                    // Data quality score
    pub data_diversity: f64,                  // Data diversity score
    pub privacy_budget: f64,                  // Privacy budget used
    pub contribution_hash: Vec<u8>,             // Contribution hash
}

Training Execution​

pub struct TrainingExecution {
    pub round_id: u64,                         // Round identifier
    pub participants: Vec<TrainingParticipant>, // Training participants
    pub training_plan: TrainingPlan,           // Training plan
    pub communication_protocol: CommunicationProtocol, // Communication protocol
    pub aggregation_method: AggregationMethod, // Aggregation method
    pub privacy_preservation: PrivacyPreservation, // Privacy preservation
}

pub struct TrainingPlan {
    pub epochs_per_round: u32,                 // Epochs per round
    pub batch_size: u32,                       // Batch size
    pub learning_rate: f64,                    // Learning rate
    pub optimization_algorithm: OptimizationAlgorithm, // Optimization algorithm
    pub convergence_criteria: ConvergenceCriteria, // Convergence criteria
    pub resource_allocation: ResourceAllocation, // Resource allocation
}

pub struct PrivacyPreservation {
    pub differential_privacy: DifferentialPrivacy, // Differential privacy
    pub secure_aggregation: SecureAggregation,   // Secure aggregation
    pub data_encryption: DataEncryption,       // Data encryption
    pub noise_injection: NoiseInjection,         // Noise injection
    pub audit_logging: AuditLogging,           // Audit logging
}

Aggregator Management​

Aggregator Whitelist​

pub struct AggregatorWhitelist {
    pub approved_aggregators: Vec<ApprovedAggregator>, // Approved aggregators
    pub reputation_requirements: ReputationRequirements, // Reputation requirements
    pub technical_requirements: TechnicalRequirements, // Technical requirements
    pub security_requirements: SecurityRequirements, // Security requirements
    pub performance_requirements: PerformanceRequirements, // Performance requirements
}

pub struct ApprovedAggregator {
    pub aggregator_id: Vec<u8>,               // Aggregator identifier
    pub operator: Vec<u8],                     // Operator address
    pub reputation_score: f64,                 // Reputation score
    pub technical_capabilities: TechnicalCapabilities, // Technical capabilities
    pub security_certifications: Vec<SecurityCertification>, // Security certifications
    pub performance_metrics: AggregatorPerformance, // Performance metrics
    pub whitelist_expiry: u64,                 // Whitelist expiry
}

pub struct TechnicalCapabilities {
    pub max_participants: u32,                 // Maximum participants
    pub supported_model_types: Vec<ModelType>, // Supported model types
    pub aggregation_methods: Vec<AggregationMethod>, // Aggregation methods
    pub communication_protocols: Vec<CommunicationProtocol>, // Communication protocols
    pub compute_resources: ComputeResources,   // Compute resources
    pub network_bandwidth: u64,                 // Network bandwidth
}

Aggregator Performance​

pub struct AggregatorPerformance {
    pub rounds_completed: u64,                  // Rounds completed
    pub average_participants: f64,              // Average participants
    pub average_accuracy: f64,                 // Average accuracy
    pub average_training_time: Duration,        // Average training time
    pub success_rate: f64,                      // Success rate
    pub participant_satisfaction: f64,         // Participant satisfaction
    pub security_incidents: u64,               // Security incidents
}

impl AggregatorPerformance {
    pub fn calculate_performance_score(&self) -> f64 {
        let success_weight = 0.3;
        let accuracy_weight = 0.25;
        let participation_weight = 0.2;
        let speed_weight = 0.15;
        let security_weight = 0.1;
        
        let success_score = self.success_rate * success_weight;
        let accuracy_score = self.average_accuracy * accuracy_weight;
        let participation_score = (self.average_participants / 100.0) * participation_weight;
        let speed_score = (1.0 / self.average_training_time.as_secs_f64() / 3600.0) * speed_weight;
        let security_score = (1.0 - (self.security_incidents as f64 / self.rounds_completed as f64)) * security_weight;
        
        success_score + accuracy_score + participation_score + speed_score + security_score
    }
}

Reward Distribution​

Reward Calculation​

pub struct RewardCalculator {
    pub base_reward_rate: f64,                  // Base reward rate
    pub quality_multiplier: f64,               // Quality multiplier
    pub participation_multiplier: f64,          // Participation multiplier
    pub reputation_multiplier: f64,             // Reputation multiplier
    pub privacy_bonus: f64,                    // Privacy bonus
}

impl RewardCalculator {
    pub fn calculate_participant_reward(&self, participant: &Participant, round: &TrainingRound) -> u128 {
        // 1. Base reward
        let base_reward = self.base_reward_rate * participant.data_contribution.data_points as f64;
        
        // 2. Quality multiplier
        let quality_multiplier = participant.data_contribution.data_quality * self.quality_multiplier;
        
        // 3. Participation multiplier
        let participation_multiplier = if participant.availability.is_available() {
            self.participation_multiplier
        } else {
            self.participation_multiplier * 0.5
        };
        
        // 4. Reputation multiplier
        let reputation_multiplier = participant.reputation_score * self.reputation_multiplier;
        
        // 5. Privacy bonus
        let privacy_bonus = match participant.privacy_level {
            PrivacyLevel::High => self.privacy_bonus * 2.0,
            PrivacyLevel::Medium => self.privacy_bonus * 1.5,
            PrivacyLevel::Low => self.privacy_bonus * 1.0,
        };
        
        let total_reward = base_reward * quality_multiplier * participation_multiplier * reputation_multiplier + privacy_bonus;
        
        total_reward as u128
    }
    
    pub fn calculate_aggregator_reward(&self, aggregator: &ApprovedAggregator, round: &TrainingRound) -> u128 {
        // 1. Base aggregator reward
        let base_reward = self.base_reward_rate * round.participants.len() as f64 * 10.0; // 10x participant reward
        
        // 2. Performance multiplier
        let performance_multiplier = aggregator.performance_metrics.calculate_performance_score();
        
        // 3. Reputation multiplier
        let reputation_multiplier = aggregator.reputation_score * self.reputation_multiplier;
        
        let total_reward = base_reward * performance_multiplier * reputation_multiplier;
        
        total_reward as u128
    }
}

Reward Distribution​

pub struct RewardDistributor {
    pub treasury: Treasury,                     // Treasury
    pub reward_pool: RewardPool,               // Reward pool
    pub distribution_schedule: DistributionSchedule, // Distribution schedule
}

impl RewardDistributor {
    pub fn distribute_rewards(&mut self, round: &TrainingRound) -> Result<RewardDistribution, DistributionError> {
        // 1. Calculate total rewards
        let participant_rewards = self.calculate_participant_rewards(round)?;
        let aggregator_reward = self.calculate_aggregator_reward(round)?;
        let treasury_fee = self.calculate_treasury_fee(round)?;
        
        // 2. Validate reward pool
        let total_rewards = participant_rewards + aggregator_reward + treasury_fee;
        if total_rewards > self.reward_pool.available_balance {
            return Err(DistributionError::InsufficientRewards);
        }
        
        // 3. Distribute rewards
        self.distribute_participant_rewards(&participant_rewards)?;
        self.distribute_aggregator_reward(aggregator_reward)?;
        self.distribute_treasury_fee(treasury_fee)?;
        
        // 4. Update reward pool
        self.reward_pool.available_balance -= total_rewards;
        
        Ok(RewardDistribution {
            participant_rewards,
            aggregator_reward,
            treasury_fee,
            total_distributed: total_rewards,
            distribution_timestamp: current_timestamp(),
        })
    }
}

Security Features​

Model Security​

pub struct ModelSecurity {
    pub vulnerability_scanner: VulnerabilityScanner, // Vulnerability scanner
    pub code_analyzer: CodeAnalyzer,           // Code analyzer
    pub privacy_checker: PrivacyChecker,       // Privacy checker
    pub compliance_validator: ComplianceValidator, // Compliance validator
}

impl ModelSecurity {
    pub fn security_scan_model(&self, model: &ModelRegistration) -> Result<SecurityReport, SecurityError> {
        // 1. Vulnerability scanning
        let vuln_report = self.vulnerability_scanner.scan_model(&model.model_id, &model.model_hash)?;
        
        // 2. Code analysis
        let code_report = self.code_analyzer.analyze_code(&model.model_id, &model.model_hash)?;
        
        // 3. Privacy checking
        let privacy_report = self.privacy_checker.check_privacy(&model.model_id, &model.model_hash)?;
        
        // 4. Compliance validation
        let compliance_report = self.compliance_validator.validate_compliance(&model.model_id, &model.model_hash)?;
        
        // 5. Generate security report
        let security_report = SecurityReport {
            model_id: model.model_id.clone(),
            vulnerability_score: vuln_report.score,
            code_quality_score: code_report.score,
            privacy_score: privacy_report.score,
            compliance_score: compliance_report.score,
            overall_score: (vuln_report.score + code_report.score + privacy_report.score + compliance_report.score) / 4.0,
            recommendations: self.generate_recommendations(&vuln_report, &code_report, &privacy_report, &compliance_report),
            scan_timestamp: current_timestamp(),
        };
        
        Ok(security_report)
    }
}

Data Privacy​

pub struct DataPrivacy {
    pub differential_privacy: DifferentialPrivacy, // Differential privacy
    pub secure_multiparty_computation: SMPC,     // Secure multiparty computation
    pub homomorphic_encryption: HomomorphicEncryption, // Homomorphic encryption
    pub zero_knowledge_proofs: ZeroKnowledgeProofs, // Zero knowledge proofs
}

impl DataPrivacy {
    pub fn apply_privacy_preservation(&self, data: &TrainingData) -> Result<PrivateData, PrivacyError> {
        // 1. Apply differential privacy
        let noisy_data = self.differential_privacy.add_noise(data)?;
        
        // 2. Apply secure aggregation
        let aggregated_data = self.secure_multiparty_computation.aggregate(noisy_data)?;
        
        // 3. Apply homomorphic encryption
        let encrypted_data = self.homomorphic_encryption.encrypt(aggregated_data)?;
        
        // 4. Generate zero knowledge proof
        let proof = self.zero_knowledge_proofs.generate_proof(&encrypted_data)?;
        
        Ok(PrivateData {
            encrypted_data,
            proof,
            privacy_level: self.calculate_privacy_level(),
            timestamp: current_timestamp(),
        })
    }
}

Performance Metrics​

Training Metrics​

pub struct TrainingMetrics {
    pub rounds_completed: u64,                  // Rounds completed
    pub average_participants: f64,              // Average participants
    pub average_accuracy: f64,                 // Average accuracy
    pub average_training_time: Duration,        // Average training time
    pub data_points_processed: u64,             // Data points processed
    pub model_improvement: f64,                 // Model improvement
}

pub struct PerformanceMetrics {
    pub throughput: f64,                       // Models per day
    pub latency_p50: Duration,                  // 50th percentile latency
    pub latency_p95: Duration,                  // 95th percentile latency
    pub resource_utilization: ResourceUtilization, // Resource utilization
    pub error_rate: f64,                       // Error rate
    pub participant_satisfaction: f64,         // Participant satisfaction
}

Economic Metrics​

pub struct EconomicMetrics {
    pub total_rewards_distributed: u128,         // Total rewards distributed
    pub average_reward_per_participant: u128,   // Average reward per participant
    pub treasury_revenue: u128,                 // Treasury revenue
    pub model_value_created: u128,             // Model value created
    pub cost_efficiency: f64,                  // Cost efficiency
    pub roi_metrics: ROIMetrics,                // ROI metrics
}

pub struct ROIMetrics {
    pub participant_roi: f64,                   // Participant ROI
    pub aggregator_roi: f64,                    // Aggregator ROI
    pub network_roi: f64,                       // Network ROI
    pub investment_payback_period: Duration,      // Investment payback period
    pub net_present_value: u128,                // Net present value
}

This FL governance system provides a comprehensive framework for decentralized AI model training while maintaining privacy, security, and fair reward distribution.