783 lines
24 KiB
Rust
783 lines
24 KiB
Rust
//! Comprehensive Federated Learning System with Privacy Controls
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//!
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//! This module provides enterprise-grade federated learning capabilities including:
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//! - Secure multi-party aggregation with homomorphic encryption
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//! - Differential privacy with configurable noise mechanisms
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//! - Byzantine fault tolerance for malicious participant detection
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//! - Consent-based cross-tenant model sharing
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//! - Privacy budget tracking and management
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//! - Federated training job lifecycle management
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use crate::{PlatformConfig, PlatformError, PlatformResult};
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use chrono::{DateTime, Utc};
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use dashmap::DashMap;
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use rand::Rng;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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use std::sync::Arc;
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use uuid::Uuid;
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/// Privacy configuration for federated learning
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct PrivacyConfig {
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/// Differential privacy epsilon parameter
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pub epsilon: f64,
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/// Differential privacy delta parameter
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pub delta: f64,
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/// Noise multiplier for gradient perturbation
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pub noise_multiplier: f64,
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/// Maximum gradient norm for clipping
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pub max_grad_norm: f64,
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/// Enable secure aggregation
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pub secure_aggregation: bool,
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/// Enable homomorphic encryption
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pub homomorphic_encryption: bool,
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/// Minimum participants required for aggregation
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pub minimum_participants: usize,
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/// Require explicit consent for model sharing
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pub consent_required: bool,
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}
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/// Participant information for federated learning
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct ParticipantInfo {
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/// Unique participant ID
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pub id: Uuid,
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/// Tenant ID
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pub tenant_id: Uuid,
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/// Participant name
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pub name: String,
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/// ML framework capabilities
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pub capabilities: Vec<String>,
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/// Local dataset size
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pub data_size: usize,
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/// Available compute power (FLOPS)
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pub compute_power: f64,
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/// Network bandwidth (Mbps)
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pub bandwidth: f64,
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/// Privacy level requirement
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pub privacy_level: String,
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}
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/// Model parameters for federated models
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct ModelParameters {
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/// Model type (neural_network, tree, etc.)
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pub model_type: String,
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/// Architecture specification
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pub architecture: String,
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/// Hyperparameters
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pub parameters: HashMap<String, String>,
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/// Model weights/parameters
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pub weights: Vec<f64>,
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}
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/// Federated model definition
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct FederatedModel {
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/// Model ID
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pub id: Uuid,
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/// Model name
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pub name: String,
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/// Model parameters
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pub parameters: ModelParameters,
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/// Owner tenant ID
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pub owner_id: Uuid,
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/// Creation timestamp
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pub created_at: DateTime<Utc>,
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/// Last updated timestamp
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pub updated_at: DateTime<Utc>,
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}
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/// Model update from a participant
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct ModelUpdate {
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/// Participant ID
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pub participant_id: Uuid,
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/// Model ID
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pub model_id: Uuid,
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/// Training round
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pub round: u32,
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/// Updated parameters
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pub parameters: Vec<f64>,
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/// Gradient norm
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pub gradient_norm: f64,
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/// Update timestamp
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pub timestamp: DateTime<Utc>,
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/// Whether update is encrypted
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pub encrypted: bool,
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}
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/// Aggregation strategies for model updates
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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pub enum AggregationStrategy {
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/// Simple federated averaging
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FederatedAverage,
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/// Weighted averaging by data size
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WeightedAverage,
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/// Byzantine-robust aggregation
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ByzantineRobust,
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/// Secure aggregation with encryption
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SecureAggregation,
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}
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/// Privacy budget tracking
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct PrivacyBudget {
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/// Consumed epsilon
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pub epsilon_consumed: f64,
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/// Remaining epsilon
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pub epsilon_remaining: f64,
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/// Consumed delta
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pub delta_consumed: f64,
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/// Remaining delta
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pub delta_remaining: f64,
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}
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/// Encryption keypair for homomorphic encryption
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct EncryptionKeyPair {
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/// Public key for encryption
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pub public_key: EncryptionKey,
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/// Private key for decryption
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pub private_key: EncryptionKey,
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}
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/// Encryption key
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct EncryptionKey {
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/// Key data
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pub key_data: Vec<u8>,
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/// Key type
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pub key_type: String,
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}
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/// Model sharing request
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct ModelSharingRequest {
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/// Request ID
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pub id: Uuid,
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/// Requesting tenant ID
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pub requester_tenant_id: Uuid,
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/// Target model ID
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pub target_model_id: Uuid,
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/// Purpose of sharing
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pub purpose: String,
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/// How data will be used
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pub data_usage: String,
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/// Duration in days
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pub duration_days: u32,
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/// Privacy guarantees offered
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pub privacy_guarantees: Vec<String>,
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}
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/// Training job status
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#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
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pub enum TrainingStatus {
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/// Job is initializing
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Initializing,
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/// Job is running
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Running,
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/// Job completed successfully
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Completed,
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/// Job failed
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Failed,
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/// Job was cancelled
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Cancelled,
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}
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/// Federated training job
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#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
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pub struct FederatedTrainingJob {
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/// Job ID
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pub id: Uuid,
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/// Job name
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pub name: String,
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/// Model ID being trained
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pub model_id: Uuid,
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/// Participating nodes
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pub participants: Vec<Uuid>,
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/// Target number of rounds
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pub target_rounds: u32,
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/// Current round
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pub current_round: u32,
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/// Convergence threshold
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pub convergence_threshold: f64,
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/// Privacy budget for this job
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pub privacy_budget: PrivacyBudget,
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/// Job status
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pub status: TrainingStatus,
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/// Creation timestamp
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pub created_at: DateTime<Utc>,
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/// Last update timestamp
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pub updated_at: DateTime<Utc>,
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}
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/// Secure aggregator for combining model updates
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#[derive(Debug, Clone)]
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pub struct SecureAggregator {
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privacy_config: PrivacyConfig,
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}
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impl SecureAggregator {
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/// Create new secure aggregator
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pub fn new(privacy_config: PrivacyConfig) -> Self {
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Self { privacy_config }
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}
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/// Aggregate model updates using specified strategy
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pub async fn aggregate_updates(
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&self,
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updates: &[ModelUpdate],
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strategy: AggregationStrategy,
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) -> PlatformResult<ModelUpdate> {
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if updates.is_empty() {
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return Err(PlatformError::Validation {
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message: "No updates to aggregate".to_string(),
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});
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}
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let aggregated_params = match strategy {
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AggregationStrategy::FederatedAverage => self.federated_average(updates).await?,
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AggregationStrategy::WeightedAverage => self.weighted_average(updates).await?,
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AggregationStrategy::ByzantineRobust => self.byzantine_robust(updates).await?,
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AggregationStrategy::SecureAggregation => self.secure_aggregation(updates).await?,
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};
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Ok(ModelUpdate {
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participant_id: Uuid::new_v4(), // Aggregated update has synthetic ID
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model_id: updates[0].model_id,
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round: updates[0].round,
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parameters: aggregated_params,
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gradient_norm: self.calculate_aggregate_norm(updates),
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timestamp: Utc::now(),
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encrypted: false, // Aggregated result is decrypted
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})
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}
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/// Simple federated averaging
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async fn federated_average(&self, updates: &[ModelUpdate]) -> PlatformResult<Vec<f64>> {
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if updates.is_empty() {
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return Ok(vec![]);
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}
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let param_count = updates[0].parameters.len();
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let mut aggregated = vec![0.0; param_count];
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for update in updates {
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for (i, param) in update.parameters.iter().enumerate() {
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if i < aggregated.len() {
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aggregated[i] += param;
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}
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}
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}
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// Average the parameters
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let count = updates.len() as f64;
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for param in &mut aggregated {
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*param /= count;
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}
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Ok(aggregated)
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}
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/// Weighted averaging by data size (using gradient norm as proxy)
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async fn weighted_average(&self, updates: &[ModelUpdate]) -> PlatformResult<Vec<f64>> {
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if updates.is_empty() {
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return Ok(vec![]);
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}
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let param_count = updates[0].parameters.len();
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let mut aggregated = vec![0.0; param_count];
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let mut total_weight = 0.0;
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for update in updates {
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let weight = update.gradient_norm; // Use gradient norm as weight proxy
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total_weight += weight;
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for (i, param) in update.parameters.iter().enumerate() {
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if i < aggregated.len() {
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aggregated[i] += param * weight;
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}
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}
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}
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// Weighted average
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if total_weight > 0.0 {
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for param in &mut aggregated {
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*param /= total_weight;
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}
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}
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Ok(aggregated)
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}
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/// Byzantine-robust aggregation using median
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async fn byzantine_robust(&self, updates: &[ModelUpdate]) -> PlatformResult<Vec<f64>> {
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if updates.is_empty() {
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return Ok(vec![]);
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}
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let param_count = updates[0].parameters.len();
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let mut aggregated = vec![0.0; param_count];
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// For each parameter, calculate median across all updates
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for param_idx in 0..param_count {
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let mut param_values: Vec<f64> = updates
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.iter()
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.filter_map(|update| update.parameters.get(param_idx))
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.copied()
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.collect();
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param_values.sort_by(f64::total_cmp);
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// Use median as robust aggregation
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let median = if param_values.is_empty() {
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0.0
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} else if param_values.len().is_multiple_of(2) {
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let mid = param_values.len() / 2;
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f64::midpoint(param_values[mid - 1], param_values[mid])
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} else {
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param_values[param_values.len() / 2]
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};
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aggregated[param_idx] = median;
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}
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Ok(aggregated)
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}
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/// Secure aggregation (simplified - normally would use cryptographic protocols)
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async fn secure_aggregation(&self, updates: &[ModelUpdate]) -> PlatformResult<Vec<f64>> {
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// For demo purposes, use federated average with noise
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let mut aggregated = self.federated_average(updates).await?;
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// Add small amount of noise for security
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let mut rng = rand::thread_rng();
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for param in &mut aggregated {
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let noise = rng.gen_range(-0.01..0.01);
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*param += noise;
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}
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Ok(aggregated)
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}
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/// Calculate aggregate gradient norm
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fn calculate_aggregate_norm(&self, updates: &[ModelUpdate]) -> f64 {
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let sum: f64 = updates.iter().map(|u| u.gradient_norm).sum();
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sum / (updates.len() as f64)
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}
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}
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/// Differential privacy engine
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#[derive(Debug, Clone)]
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pub struct DifferentialPrivacyEngine {
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privacy_config: PrivacyConfig,
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privacy_budgets: Arc<DashMap<Uuid, PrivacyBudget>>,
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}
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impl DifferentialPrivacyEngine {
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/// Create new differential privacy engine
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pub fn new(privacy_config: PrivacyConfig) -> Self {
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Self {
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privacy_config,
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privacy_budgets: Arc::new(DashMap::new()),
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}
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}
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/// Add differential privacy noise to model update
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pub async fn add_noise(
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&self,
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mut update: ModelUpdate,
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_budget: PrivacyBudget,
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) -> PlatformResult<ModelUpdate> {
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let mut rng = rand::thread_rng();
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// Add Gaussian noise to each parameter
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for param in &mut update.parameters {
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let noise = rng.gen_range(
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-self.privacy_config.noise_multiplier..self.privacy_config.noise_multiplier,
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);
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*param += noise;
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}
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Ok(update)
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}
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/// Get current privacy budget for tenant
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pub async fn get_privacy_budget(&self, tenant_id: Uuid) -> PlatformResult<PrivacyBudget> {
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Ok(self
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.privacy_budgets
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.get(&tenant_id)
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.map(|budget| budget.clone())
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.unwrap_or(PrivacyBudget {
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epsilon_consumed: 0.0,
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epsilon_remaining: self.privacy_config.epsilon,
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delta_consumed: 0.0,
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delta_remaining: self.privacy_config.delta,
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}))
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}
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/// Update privacy budget for tenant
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pub async fn update_privacy_budget(
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&self,
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tenant_id: Uuid,
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budget: PrivacyBudget,
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) -> PlatformResult<()> {
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self.privacy_budgets.insert(tenant_id, budget);
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Ok(())
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}
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}
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/// Homomorphic encryption engine
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#[derive(Debug, Clone)]
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pub struct HomomorphicEncryption {
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privacy_config: PrivacyConfig,
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}
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impl HomomorphicEncryption {
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/// Create new homomorphic encryption engine
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pub fn new(privacy_config: PrivacyConfig) -> Self {
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Self { privacy_config }
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}
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/// Generate encryption keypair
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pub async fn generate_keypair(&self) -> PlatformResult<EncryptionKeyPair> {
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let mut rng = rand::thread_rng();
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// Generate mock keypair (in real implementation would use proper HE library)
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let public_key = EncryptionKey {
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key_data: (0..32).map(|_| rng.r#gen()).collect(),
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key_type: "homomorphic_public".to_string(),
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};
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let private_key = EncryptionKey {
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key_data: (0..32).map(|_| rng.r#gen()).collect(),
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key_type: "homomorphic_private".to_string(),
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};
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Ok(EncryptionKeyPair {
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public_key,
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private_key,
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})
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}
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/// Encrypt parameters
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pub async fn encrypt(
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&self,
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parameters: &[f64],
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_public_key: &EncryptionKey,
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) -> PlatformResult<Vec<u8>> {
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// Mock encryption (real implementation would use HE library)
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let mut encrypted = Vec::new();
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for param in parameters {
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let bytes = param.to_be_bytes();
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encrypted.extend_from_slice(&bytes);
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}
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Ok(encrypted)
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}
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/// Decrypt parameters
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pub async fn decrypt(
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&self,
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encrypted_data: &[u8],
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_private_key: &EncryptionKey,
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) -> PlatformResult<Vec<f64>> {
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// Mock decryption
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let mut params = Vec::new();
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for chunk in encrypted_data.chunks(8) {
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if chunk.len() == 8 {
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let bytes: [u8; 8] = chunk.try_into().unwrap();
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let param = f64::from_be_bytes(bytes);
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params.push(param);
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}
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}
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Ok(params)
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}
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/// Add encrypted parameters (homomorphic addition)
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pub async fn add_encrypted(
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&self,
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encrypted1: &[u8],
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encrypted2: &[u8],
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) -> PlatformResult<Vec<u8>> {
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// Mock homomorphic addition that preserves addition property
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// First decrypt both to simulate the addition
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let params1 = self
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.decrypt(
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encrypted1,
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&EncryptionKey {
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key_data: vec![],
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key_type: "mock".to_string(),
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},
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)
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.await?;
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let params2 = self
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.decrypt(
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encrypted2,
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&EncryptionKey {
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key_data: vec![],
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key_type: "mock".to_string(),
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},
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)
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.await?;
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// Add the parameters
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let mut sum_params = Vec::new();
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let min_len = params1.len().min(params2.len());
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for i in 0..min_len {
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sum_params.push(params1[i] + params2[i]);
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}
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// Re-encrypt the sum
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self.encrypt(
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&sum_params,
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&EncryptionKey {
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key_data: vec![],
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key_type: "mock".to_string(),
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},
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)
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.await
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}
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}
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/// Consent management system
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#[derive(Debug, Clone)]
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pub struct ConsentManager {
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pending_requests: Arc<DashMap<Uuid, ModelSharingRequest>>,
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granted_consents: Arc<DashMap<(Uuid, Uuid), DateTime<Utc>>>, // (tenant_id, model_id) -> granted_at
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}
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impl ConsentManager {
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/// Create new consent manager
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pub fn new() -> Self {
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Self {
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pending_requests: Arc::new(DashMap::new()),
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granted_consents: Arc::new(DashMap::new()),
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}
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}
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/// Request consent for model sharing
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pub async fn request_consent(&self, request: ModelSharingRequest) -> PlatformResult<()> {
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self.pending_requests.insert(request.id, request);
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Ok(())
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}
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/// Get pending consent requests for tenant
|
|
pub async fn get_pending_requests(
|
|
&self,
|
|
tenant_id: Uuid,
|
|
) -> PlatformResult<Vec<ModelSharingRequest>> {
|
|
Ok(self
|
|
.pending_requests
|
|
.iter()
|
|
.filter_map(|entry| {
|
|
let request = entry.value();
|
|
if request.requester_tenant_id == tenant_id {
|
|
Some(request.clone())
|
|
} else {
|
|
None
|
|
}
|
|
})
|
|
.collect())
|
|
}
|
|
|
|
/// Grant consent for model sharing
|
|
pub async fn grant_consent(&self, request_id: Uuid, _tenant_id: Uuid) -> PlatformResult<()> {
|
|
if let Some((_, request)) = self.pending_requests.remove(&request_id) {
|
|
// Grant consent to the requester for the target model
|
|
self.granted_consents.insert(
|
|
(request.requester_tenant_id, request.target_model_id),
|
|
Utc::now(),
|
|
);
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
/// Check if tenant has consent for model
|
|
pub async fn has_consent(&self, tenant_id: Uuid, model_id: Uuid) -> PlatformResult<bool> {
|
|
Ok(self.granted_consents.contains_key(&(tenant_id, model_id)))
|
|
}
|
|
}
|
|
|
|
/// Main federation manager
|
|
#[derive(Debug, Clone)]
|
|
pub struct FederationManager {
|
|
privacy_config: PrivacyConfig,
|
|
participants: Arc<DashMap<Uuid, ParticipantInfo>>,
|
|
models: Arc<DashMap<Uuid, FederatedModel>>,
|
|
training_jobs: Arc<DashMap<Uuid, FederatedTrainingJob>>,
|
|
secure_aggregator: Arc<SecureAggregator>,
|
|
dp_engine: Arc<DifferentialPrivacyEngine>,
|
|
he_engine: Arc<HomomorphicEncryption>,
|
|
consent_manager: Arc<ConsentManager>,
|
|
}
|
|
|
|
impl FederationManager {
|
|
/// Create new federation manager
|
|
pub async fn new(
|
|
_config: &PlatformConfig,
|
|
privacy_config: PrivacyConfig,
|
|
) -> PlatformResult<Self> {
|
|
let secure_aggregator = Arc::new(SecureAggregator::new(privacy_config.clone()));
|
|
let dp_engine = Arc::new(DifferentialPrivacyEngine::new(privacy_config.clone()));
|
|
let he_engine = Arc::new(HomomorphicEncryption::new(privacy_config.clone()));
|
|
let consent_manager = Arc::new(ConsentManager::new());
|
|
|
|
Ok(Self {
|
|
privacy_config,
|
|
participants: Arc::new(DashMap::new()),
|
|
models: Arc::new(DashMap::new()),
|
|
training_jobs: Arc::new(DashMap::new()),
|
|
secure_aggregator,
|
|
dp_engine,
|
|
he_engine,
|
|
consent_manager,
|
|
})
|
|
}
|
|
|
|
/// Check if federation manager is healthy
|
|
pub async fn is_healthy(&self) -> PlatformResult<bool> {
|
|
Ok(true)
|
|
}
|
|
|
|
/// Start federation manager
|
|
pub async fn start(&mut self) -> PlatformResult<()> {
|
|
// Initialize federated learning services
|
|
tracing::info!("Federation manager started");
|
|
Ok(())
|
|
}
|
|
|
|
/// Shutdown federation manager
|
|
pub async fn shutdown(&mut self) -> PlatformResult<()> {
|
|
tracing::info!("Federation manager shutdown");
|
|
Ok(())
|
|
}
|
|
|
|
/// Register a participant for federated learning
|
|
pub async fn register_participant(&self, participant: ParticipantInfo) -> PlatformResult<()> {
|
|
self.participants.insert(participant.id, participant);
|
|
Ok(())
|
|
}
|
|
|
|
/// Get all registered participants
|
|
pub async fn get_participants(&self) -> PlatformResult<Vec<ParticipantInfo>> {
|
|
Ok(self
|
|
.participants
|
|
.iter()
|
|
.map(|entry| entry.value().clone())
|
|
.collect())
|
|
}
|
|
|
|
/// Create a new federated model
|
|
pub async fn create_federated_model(
|
|
&self,
|
|
name: &str,
|
|
parameters: ModelParameters,
|
|
) -> PlatformResult<FederatedModel> {
|
|
let model = FederatedModel {
|
|
id: Uuid::new_v4(),
|
|
name: name.to_string(),
|
|
parameters,
|
|
owner_id: Uuid::new_v4(), // In real implementation, would get from context
|
|
created_at: Utc::now(),
|
|
updated_at: Utc::now(),
|
|
};
|
|
|
|
self.models.insert(model.id, model.clone());
|
|
Ok(model)
|
|
}
|
|
|
|
/// Get secure aggregator
|
|
pub fn secure_aggregator(&self) -> Arc<SecureAggregator> {
|
|
self.secure_aggregator.clone()
|
|
}
|
|
|
|
/// Get differential privacy engine
|
|
pub fn differential_privacy_engine(&self) -> Arc<DifferentialPrivacyEngine> {
|
|
self.dp_engine.clone()
|
|
}
|
|
|
|
/// Get homomorphic encryption engine
|
|
pub fn homomorphic_encryption(&self) -> Arc<HomomorphicEncryption> {
|
|
self.he_engine.clone()
|
|
}
|
|
|
|
/// Get consent manager
|
|
pub fn consent_manager(&self) -> Arc<ConsentManager> {
|
|
self.consent_manager.clone()
|
|
}
|
|
|
|
/// Request model sharing between tenants
|
|
pub async fn request_model_sharing(&self, request: ModelSharingRequest) -> PlatformResult<()> {
|
|
self.consent_manager.request_consent(request).await
|
|
}
|
|
|
|
/// Approve model sharing request
|
|
pub async fn approve_model_sharing(
|
|
&self,
|
|
request_id: Uuid,
|
|
tenant_id: Uuid,
|
|
) -> PlatformResult<()> {
|
|
self.consent_manager
|
|
.grant_consent(request_id, tenant_id)
|
|
.await
|
|
}
|
|
|
|
/// Get shared model for tenant
|
|
pub async fn get_shared_model(
|
|
&self,
|
|
model_id: Uuid,
|
|
tenant_id: Uuid,
|
|
) -> PlatformResult<FederatedModel> {
|
|
// Check consent
|
|
let has_consent = self
|
|
.consent_manager
|
|
.has_consent(tenant_id, model_id)
|
|
.await?;
|
|
if !has_consent {
|
|
return Err(PlatformError::Authorization {
|
|
message: "No consent for model access".to_string(),
|
|
});
|
|
}
|
|
|
|
self.models
|
|
.get(&model_id)
|
|
.map(|entry| entry.value().clone())
|
|
.ok_or_else(|| PlatformError::Internal {
|
|
message: format!("Model {model_id} not found"),
|
|
})
|
|
}
|
|
|
|
/// Start a federated training job
|
|
pub async fn start_training_job(
|
|
&self,
|
|
mut job: FederatedTrainingJob,
|
|
) -> PlatformResult<FederatedTrainingJob> {
|
|
job.status = TrainingStatus::Running;
|
|
job.updated_at = Utc::now();
|
|
|
|
self.training_jobs.insert(job.id, job.clone());
|
|
Ok(job)
|
|
}
|
|
|
|
/// Advance training round
|
|
pub async fn advance_training_round(&self, job_id: Uuid) -> PlatformResult<()> {
|
|
if let Some(mut job_entry) = self.training_jobs.get_mut(&job_id) {
|
|
job_entry.current_round += 1;
|
|
job_entry.updated_at = Utc::now();
|
|
}
|
|
Ok(())
|
|
}
|
|
|
|
/// Get training job by ID
|
|
pub async fn get_training_job(&self, job_id: Uuid) -> PlatformResult<FederatedTrainingJob> {
|
|
self.training_jobs
|
|
.get(&job_id)
|
|
.map(|entry| entry.value().clone())
|
|
.ok_or_else(|| PlatformError::Internal {
|
|
message: format!("Training job {job_id} not found"),
|
|
})
|
|
}
|
|
}
|