osobhandClaude Opus 4.6
02d382d5f6
Consistent formatting pass: line wrapping, import sorting, trailing whitespace removal, let-chain indentation, merged derive attributes, and unsafe block reformatting. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]>
942 lines
32 KiB
Rust
942 lines
32 KiB
Rust
//! # RTX Federated - Advanced Federated Learning Platform
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//!
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//! A comprehensive federated learning platform that enables privacy-preserving distributed
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//! machine learning with state-of-the-art algorithms, Byzantine fault tolerance, and
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//! production-grade infrastructure.
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//!
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//! ## Features
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//!
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//! ### Advanced Aggregation Algorithms
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//! - **FedAvg**: Federated Averaging with momentum and adaptive learning
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//! - **FedProx**: Proximal federated optimization for heterogeneous networks
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//! - **SCAFFOLD**: Variance reduction with control variates for drift correction
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//! - **FedNova**: Normalized averaging for non-IID data distributions
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//! - **Asynchronous Aggregation**: Dynamic client participation support
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//!
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//! ### Personalized Federated Learning
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//! - **Meta-Learning**: MAML (Model-Agnostic Meta-Learning) for federated settings
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//! - **Personalization Layers**: Client-specific layers with shared backbone
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//! - **Multi-Task Learning**: Joint optimization across related tasks
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//! - **Client Clustering**: Data distribution-based client grouping
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//! - **Transfer Learning**: Knowledge transfer across federated clients
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//!
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//! ### Byzantine-Robust Aggregation
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//! - **Krum/Multi-Krum**: Geometric median-based Byzantine tolerance
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//! - **Trimmed Mean**: Robust statistical aggregation
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//! - **Gradient Clipping**: Defense against gradient attacks
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//! - **Anomaly Detection**: Real-time malicious update detection
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//! - **Reputation Systems**: Trust-based client weighting
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//!
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//! ### Privacy Mechanisms
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//! - **Differential Privacy**: Gaussian and Laplace noise mechanisms
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//! - **Local Differential Privacy**: Client-side privacy guarantees
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//! - **Secure Multi-Party Computation**: Privacy-preserving aggregation
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//! - **Homomorphic Encryption**: Computation on encrypted gradients
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//! - **Privacy Accounting**: Epsilon budget management and tracking
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//!
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//! ### Production Infrastructure
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//! - **Client Management**: Dynamic registration and lifecycle management
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//! - **Communication Optimization**: Gradient compression and quantization
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//! - **Fault Tolerance**: Robust recovery from client failures
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//! - **Monitoring**: Real-time analytics and performance tracking
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//! - **Resource-Aware Scheduling**: Adaptive client selection and scheduling
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//!
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//! ## Architecture
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//!
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//! ```text
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//! ┌─────────────────────────────────────────────────────────────────────┐
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//! │ RTX Federated │
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//! ├─────────────┬─────────────┬─────────────┬─────────────┬─────────────┤
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//! │ Aggregation │ Personaliza │ Byzantine │ Privacy │ Infrastruct │
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//! │ │ tion │ Robust │ │ ure │
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//! │ • FedAvg │ • Meta-Learn│ • Krum │ • Diff Priv │ • Client │
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//! │ • FedProx │ • Personal │ • Trimmed │ • Homomorph │ Mgmt │
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//! │ • SCAFFOLD │ • Multi-Task│ Mean │ • SMPC │ • Comm Opt │
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//! │ • FedNova │ • Clustering│ • Anomaly │ • Privacy │ • Fault Tol │
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//! │ • Async │ • Transfer │ Detection │ Accounting│ • Monitoring│
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//! └─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘
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//! ```
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//!
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//! ## Usage Example
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//!
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//! ```rust,ignore
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//! use rtx_federated::{
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//! FederatedSystem, FederatedConfig, AggregationAlgorithm,
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//! PrivacyMechanism, Client, ModelUpdate
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//! };
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//!
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//! #[tokio::main]
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//! async fn main() -> Result<(), Box<dyn std::error::Error>> {
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//! // Configure federated learning system
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//! let config = FederatedConfig::new()
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//! .with_aggregation(AggregationAlgorithm::FedAvg { momentum: 0.9 })
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//! .with_privacy(PrivacyMechanism::DifferentialPrivacy { epsilon: 1.0 })
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//! .with_byzantine_tolerance(true)
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//! .with_client_selection_ratio(0.3);
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//!
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//! // Initialize federated system
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//! let mut fed_system = FederatedSystem::new(config).await?;
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//!
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//! // Register clients
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//! for i in 0..100 {
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//! let client = Client::new(format!("client_{}", i)).await?;
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//! fed_system.register_client(client).await?;
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//! }
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//!
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//! // Run federated training rounds
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//! for round in 0..100 {
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//! let selected_clients = fed_system.select_clients().await?;
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//! let model_updates = fed_system.collect_updates(selected_clients).await?;
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//! let aggregated_model = fed_system.aggregate_updates(model_updates).await?;
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//! fed_system.distribute_model(aggregated_model).await?;
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//! }
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//!
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//! Ok(())
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//! }
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//! ```
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#![deny(unsafe_code)]
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#![warn(rust_2018_idioms)]
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// missing_docs is handled at workspace level
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pub mod aggregation;
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pub mod byzantine;
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pub mod error;
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pub mod infrastructure;
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pub mod personalization;
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pub mod privacy;
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pub mod simulation;
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// Re-export core types
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pub use crate::{
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aggregation::{
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AggregationAlgorithm, AggregationResult, AsyncAggregation, FedAvg, FedNova, FedProx,
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ModelUpdate, Scaffold,
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},
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byzantine::{AnomalyDetector, ByzantineRobust, Krum, MultiKrum, ReputationSystem, TrimmedMean},
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error::{FederatedError, Result},
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infrastructure::{
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ClientManager, CommunicationOptimizer, FaultTolerance, MonitoringSystem, ResourceScheduler,
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},
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personalization::{
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ClientClustering, MetaLearning, MultiTaskLearning, PersonalizationLayers, TransferLearning,
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},
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privacy::{
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DifferentialPrivacy, HomomorphicEncryption, LocalDifferentialPrivacy, PrivacyBudget,
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PrivacyMechanism, SecureMultiPartyComputation,
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},
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};
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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 tokio::sync::RwLock;
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use tracing::{debug, info, warn};
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use uuid::Uuid;
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/// Comprehensive federated learning system
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pub struct FederatedSystem {
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config: FederatedConfig,
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clients: RwLock<HashMap<Uuid, Client>>,
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aggregation_engine: Box<dyn AggregationAlgorithm + Send + Sync>,
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privacy_engine: Option<Box<dyn PrivacyMechanism + Send + Sync>>,
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byzantine_protection: Option<Box<dyn ByzantineRobust + Send + Sync>>,
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infrastructure: Infrastructure,
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metrics: RwLock<FederatedMetrics>,
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}
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/// Federated learning configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct FederatedConfig {
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/// Number of federated learning rounds
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pub num_rounds: usize,
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/// Fraction of clients selected per round
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pub client_selection_ratio: f64,
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/// Minimum number of clients required
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pub min_clients: usize,
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/// Maximum number of clients allowed
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pub max_clients: usize,
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/// Aggregation algorithm configuration
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pub aggregation: aggregation::AggregationConfig,
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/// Privacy mechanism configuration
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pub privacy: Option<PrivacyConfig>,
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/// Byzantine fault tolerance configuration
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pub byzantine_tolerance: bool,
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/// Personalization settings
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pub personalization: PersonalizationConfig,
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/// Communication optimization settings
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pub communication: CommunicationConfig,
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/// Monitoring and logging configuration
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pub monitoring: MonitoringConfig,
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}
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/// Aggregation algorithm configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum AggregationConfig {
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/// Federated Averaging with optional momentum
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FedAvg {
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momentum: Option<f64>,
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adaptive_learning_rate: bool,
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},
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/// Federated Proximal with regularization parameter
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FedProx { mu: f64, local_epochs: usize },
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/// SCAFFOLD with control variates
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Scaffold {
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learning_rate: f64,
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local_steps: usize,
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},
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/// FedNova with normalized averaging
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FedNova { tau_eff: f64, momentum: f64 },
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/// Asynchronous aggregation
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Async {
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staleness_threshold: usize,
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mixing_parameter: f64,
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},
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}
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/// Privacy mechanism configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum PrivacyConfig {
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/// Differential privacy with noise parameters
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DifferentialPrivacy {
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epsilon: f64,
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delta: f64,
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noise_mechanism: NoiseMechanism,
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},
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/// Local differential privacy
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LocalDifferentialPrivacy {
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epsilon: f64,
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randomization_mechanism: RandomizationMechanism,
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},
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/// Secure multi-party computation
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SecureMultiPartyComputation {
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threshold: usize,
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security_parameter: usize,
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},
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/// Homomorphic encryption
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HomomorphicEncryption { key_size: usize, precision: usize },
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}
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/// Noise mechanisms for differential privacy
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum NoiseMechanism {
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Gaussian { sigma: f64 },
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Laplace { scale: f64 },
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Exponential { rate: f64 },
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Discrete { sensitivity: f64 },
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}
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/// Randomization mechanisms for local differential privacy
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum RandomizationMechanism {
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RandomResponse,
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LocalHashing,
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Duchi,
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}
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/// Personalization configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct PersonalizationConfig {
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/// Enable meta-learning (MAML)
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pub meta_learning_enabled: bool,
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/// Number of personalization layers
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pub personalization_layers: usize,
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/// Enable multi-task learning
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pub multi_task_enabled: bool,
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/// Client clustering configuration
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pub clustering_config: Option<ClusteringConfig>,
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/// Transfer learning settings
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pub transfer_learning: TransferLearningConfig,
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}
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/// Client clustering configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ClusteringConfig {
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/// Number of clusters
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pub num_clusters: usize,
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/// Clustering algorithm
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pub algorithm: ClusteringAlgorithm,
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/// Re-clustering frequency (rounds)
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pub reclustering_frequency: usize,
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}
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/// Clustering algorithms
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum ClusteringAlgorithm {
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KMeans,
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SpectralClustering,
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HierarchicalClustering,
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DBScan,
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}
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/// Transfer learning configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct TransferLearningConfig {
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/// Enable transfer learning
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pub enabled: bool,
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/// Source domain similarity threshold
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pub similarity_threshold: f64,
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/// Transfer learning method
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pub method: TransferMethod,
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}
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/// Transfer learning methods
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum TransferMethod {
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FineTuning,
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FeatureExtraction,
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DomainAdaptation,
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MultiTaskLearning,
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}
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/// Communication optimization configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct CommunicationConfig {
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/// Enable gradient compression
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pub compression_enabled: bool,
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/// Compression ratio (0.0 to 1.0)
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pub compression_ratio: f64,
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/// Quantization bits
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pub quantization_bits: usize,
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/// Enable sparsification
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pub sparsification_enabled: bool,
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/// Top-k sparsification parameter
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pub top_k_ratio: f64,
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}
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/// Monitoring and logging configuration
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct MonitoringConfig {
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/// Enable real-time monitoring
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pub enabled: bool,
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/// Metrics collection interval (seconds)
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pub metrics_interval: u64,
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/// Enable performance profiling
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pub profiling_enabled: bool,
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/// Log verbosity level
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pub log_level: LogLevel,
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}
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/// Log verbosity levels
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum LogLevel {
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Error,
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Warn,
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Info,
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Debug,
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Trace,
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}
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/// Federated learning client
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#[derive(Debug, Clone)]
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pub struct Client {
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/// Unique client identifier
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pub id: Uuid,
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/// Client name/label
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pub name: String,
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/// Client capabilities
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pub capabilities: ClientCapabilities,
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/// Client status
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pub status: ClientStatus,
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/// Data characteristics
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pub data_profile: DataProfile,
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/// Communication endpoint
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pub endpoint: Option<String>,
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/// Last seen timestamp
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pub last_seen: chrono::DateTime<chrono::Utc>,
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}
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/// Client capabilities
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct ClientCapabilities {
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/// Computational power score (0.0 to 1.0)
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pub compute_score: f64,
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/// Available memory (bytes)
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pub memory_bytes: u64,
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/// Network bandwidth (Mbps)
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pub bandwidth_mbps: f64,
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/// Battery level (0.0 to 1.0, if applicable)
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pub battery_level: Option<f64>,
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/// Supported privacy mechanisms
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pub privacy_support: Vec<String>,
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}
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/// Client status
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub enum ClientStatus {
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Available,
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Training,
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Offline,
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Faulty,
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Malicious,
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}
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/// Data profile for client data characteristics
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DataProfile {
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/// Number of local samples
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pub sample_count: usize,
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/// Data distribution identifier
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pub distribution_id: Option<String>,
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/// Data quality score (0.0 to 1.0)
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pub quality_score: f64,
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/// Privacy sensitivity level
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pub privacy_level: PrivacyLevel,
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}
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/// Privacy sensitivity levels
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#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
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pub enum PrivacyLevel {
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Public,
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Internal,
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Confidential,
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Restricted,
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TopSecret,
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}
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/// Infrastructure components
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pub struct Infrastructure {
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client_manager: ClientManager,
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communication_optimizer: CommunicationOptimizer,
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fault_tolerance: FaultTolerance,
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monitoring_system: MonitoringSystem,
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resource_scheduler: ResourceScheduler,
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}
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/// Federated learning metrics
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#[derive(Debug, Default, Clone, Serialize, Deserialize)]
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pub struct FederatedMetrics {
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/// Total number of rounds completed
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pub rounds_completed: usize,
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/// Current round number
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pub current_round: usize,
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/// Number of active clients
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pub active_clients: usize,
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/// Average model accuracy across clients
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pub average_accuracy: f64,
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/// Communication overhead (bytes)
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pub communication_overhead: u64,
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/// Training time per round (milliseconds)
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pub training_time_ms: u64,
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/// Privacy budget consumed
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pub privacy_budget_consumed: f64,
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/// Byzantine attacks detected
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pub byzantine_attacks_detected: usize,
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/// System uptime (seconds)
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pub uptime_seconds: u64,
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}
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impl FederatedSystem {
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/// Create a new federated learning system
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pub async fn new(config: FederatedConfig) -> Result<Self> {
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info!("🚀 Initializing RTX Federated Learning System...");
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// Initialize aggregation engine
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let aggregation_engine = create_aggregation_engine(&config.aggregation).await?;
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// Initialize privacy engine if configured
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let privacy_engine = if let Some(privacy_config) = &config.privacy {
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Some(create_privacy_engine(privacy_config).await?)
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} else {
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None
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};
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// Initialize Byzantine protection if enabled
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let byzantine_protection = if config.byzantine_tolerance {
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Some(create_byzantine_protection().await?)
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} else {
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None
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};
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// Initialize infrastructure
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let infrastructure = Infrastructure {
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client_manager: ClientManager::new().await?,
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communication_optimizer: CommunicationOptimizer::new(
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infrastructure::CommunicationConfig::default(),
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)
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.await?,
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fault_tolerance: FaultTolerance::new().await?,
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monitoring_system: MonitoringSystem::new(infrastructure::MonitoringConfig::default())
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.await?,
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resource_scheduler: ResourceScheduler::new().await?,
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};
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info!("✅ Federated Learning System initialized successfully");
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Ok(Self {
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config,
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clients: RwLock::new(HashMap::new()),
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aggregation_engine,
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privacy_engine,
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byzantine_protection,
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infrastructure,
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metrics: RwLock::new(FederatedMetrics::default()),
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})
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}
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/// Register a new client
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pub async fn register_client(&mut self, client: Client) -> Result<()> {
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debug!("Registering client: {}", client.name);
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let client_id = client.id;
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let mut clients = self.clients.write().await;
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if clients.len() >= self.config.max_clients {
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return Err(FederatedError::MaxClientsReached(self.config.max_clients));
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}
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clients.insert(client_id, client.clone());
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// Create ClientConnection from Client
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use infrastructure::client_manager::protocol::{ClientConnection, ConnectionQuality};
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use std::net::SocketAddr;
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use std::sync::atomic::AtomicU64;
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let connection = ClientConnection {
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client_id,
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endpoint: client
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.endpoint
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.as_ref()
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.and_then(|e| e.parse::<SocketAddr>().ok())
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.unwrap_or_else(|| "127.0.0.1:8080".parse().unwrap()),
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connected_at: chrono::Utc::now(),
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last_heartbeat: chrono::Utc::now(),
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bytes_sent: Arc::new(AtomicU64::new(0)),
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bytes_received: Arc::new(AtomicU64::new(0)),
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round_trip_time_ms: Arc::new(AtomicU64::new(0)),
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connection_quality: ConnectionQuality::default(),
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auth_token: None,
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stream: None,
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};
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self.infrastructure
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.client_manager
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.register_client(client_id, connection)
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.await?;
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let mut metrics = self.metrics.write().await;
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metrics.active_clients = clients.len();
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info!("✅ Client {} registered successfully", client_id);
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Ok(())
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}
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/// Select clients for the current training round
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pub async fn select_clients(&self) -> Result<Vec<Uuid>> {
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let clients = self.clients.read().await;
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let available_clients: Vec<Uuid> = clients
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.iter()
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.filter(|(_, client)| matches!(client.status, ClientStatus::Available))
|
|
.map(|(id, _)| *id)
|
|
.collect();
|
|
|
|
if available_clients.len() < self.config.min_clients {
|
|
return Err(FederatedError::InsufficientClients {
|
|
required: self.config.min_clients,
|
|
available: available_clients.len(),
|
|
});
|
|
}
|
|
|
|
let selected_count =
|
|
(available_clients.len() as f64 * self.config.client_selection_ratio).ceil() as usize;
|
|
let selected_count = selected_count
|
|
.min(available_clients.len())
|
|
.max(self.config.min_clients);
|
|
|
|
let selected_clients = self
|
|
.infrastructure
|
|
.resource_scheduler
|
|
.select_clients(&available_clients, selected_count)
|
|
.await?;
|
|
|
|
debug!(
|
|
"Selected {} clients for training round",
|
|
selected_clients.len()
|
|
);
|
|
Ok(selected_clients)
|
|
}
|
|
|
|
/// Collect model updates from selected clients
|
|
pub async fn collect_updates(&self, client_ids: Vec<Uuid>) -> Result<Vec<ModelUpdate>> {
|
|
let mut updates = Vec::new();
|
|
|
|
for client_id in client_ids {
|
|
match self
|
|
.infrastructure
|
|
.client_manager
|
|
.request_update(client_id)
|
|
.await
|
|
{
|
|
Ok(_update_bytes) => {
|
|
// Convert raw bytes to ModelUpdate
|
|
// In a real implementation, this would deserialize the update
|
|
let model_update = ModelUpdate {
|
|
client_id,
|
|
parameters: {
|
|
let mut params = HashMap::new();
|
|
// Simulate parameter extraction from bytes
|
|
params.insert("layer1.weight".to_string(), vec![0.1, 0.2, 0.3]);
|
|
params.insert("layer1.bias".to_string(), vec![0.01, 0.02]);
|
|
params.insert("layer2.weight".to_string(), vec![0.4, 0.5, 0.6]);
|
|
params.insert("layer2.bias".to_string(), vec![0.03, 0.04]);
|
|
params
|
|
},
|
|
sample_count: 1000, // Number of samples client trained on
|
|
loss: 0.05, // Training loss achieved
|
|
accuracy: 0.95, // Training accuracy achieved
|
|
training_time_ms: 5000, // Training time in milliseconds
|
|
local_epochs: 5, // Local epochs completed
|
|
learning_rate: 0.001, // Learning rate used
|
|
timestamp: chrono::Utc::now(),
|
|
metadata: HashMap::new(), // Additional metadata
|
|
};
|
|
|
|
// Apply privacy mechanism if configured
|
|
let processed_update = if let Some(privacy_engine) = &self.privacy_engine {
|
|
privacy_engine.apply_privacy(&model_update).await?
|
|
} else {
|
|
model_update
|
|
};
|
|
updates.push(processed_update);
|
|
}
|
|
Err(e) => {
|
|
warn!("Failed to collect update from client {}: {}", client_id, e);
|
|
continue;
|
|
}
|
|
}
|
|
}
|
|
|
|
if updates.is_empty() {
|
|
return Err(FederatedError::NoUpdatesCollected);
|
|
}
|
|
|
|
debug!("Collected {} model updates", updates.len());
|
|
Ok(updates)
|
|
}
|
|
|
|
/// Aggregate model updates using the configured algorithm
|
|
pub async fn aggregate_updates(&self, updates: Vec<ModelUpdate>) -> Result<ModelUpdate> {
|
|
// Apply Byzantine protection if enabled
|
|
let filtered_updates = if let Some(byzantine_protection) = &self.byzantine_protection {
|
|
byzantine_protection.filter_updates(&updates).await?
|
|
} else {
|
|
updates
|
|
};
|
|
|
|
// Perform aggregation
|
|
let aggregated = self.aggregation_engine.aggregate(&filtered_updates).await?;
|
|
|
|
debug!("Successfully aggregated {} updates", filtered_updates.len());
|
|
Ok(aggregated)
|
|
}
|
|
|
|
/// Distribute aggregated model to clients
|
|
pub async fn distribute_model(&self, model: ModelUpdate) -> Result<()> {
|
|
let clients = self.clients.read().await;
|
|
let mut distribution_tasks = Vec::new();
|
|
|
|
for (client_id, _) in clients.iter() {
|
|
let compressed_model = self
|
|
.infrastructure
|
|
.communication_optimizer
|
|
.compress_model(&model)
|
|
.await?;
|
|
|
|
// Serialize the model to bytes for distribution
|
|
let model_bytes = bincode::serialize(&compressed_model).unwrap_or_else(|_| Vec::new());
|
|
|
|
let task = self
|
|
.infrastructure
|
|
.client_manager
|
|
.distribute_model(*client_id, model_bytes);
|
|
distribution_tasks.push(task);
|
|
}
|
|
|
|
// Wait for all distributions to complete
|
|
let results = futures::future::join_all(distribution_tasks).await;
|
|
|
|
let successful_distributions = results
|
|
.into_iter()
|
|
.filter(std::result::Result::is_ok)
|
|
.count();
|
|
|
|
info!(
|
|
"Distributed model to {}/{} clients",
|
|
successful_distributions,
|
|
clients.len()
|
|
);
|
|
Ok(())
|
|
}
|
|
|
|
/// Run a complete federated learning round
|
|
pub async fn run_round(&mut self) -> Result<FederatedMetrics> {
|
|
let start_time = std::time::Instant::now();
|
|
let mut metrics = self.metrics.write().await;
|
|
metrics.current_round += 1;
|
|
let current_round = metrics.current_round;
|
|
drop(metrics);
|
|
|
|
info!("🔄 Starting federated learning round {}", current_round);
|
|
|
|
// Select clients
|
|
let selected_clients = self.select_clients().await?;
|
|
|
|
// Collect updates
|
|
let updates = self.collect_updates(selected_clients).await?;
|
|
|
|
// Aggregate updates
|
|
let aggregated_model = self.aggregate_updates(updates).await?;
|
|
|
|
// Distribute aggregated model
|
|
self.distribute_model(aggregated_model).await?;
|
|
|
|
// Update metrics
|
|
let mut metrics = self.metrics.write().await;
|
|
metrics.rounds_completed += 1;
|
|
metrics.training_time_ms = start_time.elapsed().as_millis() as u64;
|
|
|
|
info!(
|
|
"✅ Completed federated learning round {} in {:?}",
|
|
current_round,
|
|
start_time.elapsed()
|
|
);
|
|
Ok(metrics.clone())
|
|
}
|
|
|
|
/// Get current system metrics
|
|
pub async fn get_metrics(&self) -> FederatedMetrics {
|
|
self.metrics.read().await.clone()
|
|
}
|
|
|
|
/// Shutdown the federated learning system
|
|
pub async fn shutdown(&mut self) -> Result<()> {
|
|
info!("🛑 Shutting down federated learning system...");
|
|
|
|
self.infrastructure.monitoring_system.shutdown().await?;
|
|
self.infrastructure.client_manager.shutdown().await?;
|
|
|
|
info!("✅ Federated learning system shutdown complete");
|
|
Ok(())
|
|
}
|
|
}
|
|
|
|
impl FederatedConfig {
|
|
/// Create a new federated learning configuration with defaults
|
|
pub fn new() -> Self {
|
|
Self {
|
|
num_rounds: 100,
|
|
client_selection_ratio: 0.1,
|
|
min_clients: 2,
|
|
max_clients: 1000,
|
|
aggregation: aggregation::AggregationConfig::FedAvg {
|
|
momentum: Some(0.9),
|
|
adaptive_learning_rate: true,
|
|
weight_decay: None,
|
|
},
|
|
privacy: Some(PrivacyConfig::DifferentialPrivacy {
|
|
epsilon: 1.0,
|
|
delta: 1e-5,
|
|
noise_mechanism: NoiseMechanism::Gaussian { sigma: 1.0 },
|
|
}),
|
|
byzantine_tolerance: true,
|
|
personalization: PersonalizationConfig {
|
|
meta_learning_enabled: false,
|
|
personalization_layers: 2,
|
|
multi_task_enabled: false,
|
|
clustering_config: None,
|
|
transfer_learning: TransferLearningConfig {
|
|
enabled: false,
|
|
similarity_threshold: 0.8,
|
|
method: TransferMethod::FineTuning,
|
|
},
|
|
},
|
|
communication: CommunicationConfig {
|
|
compression_enabled: true,
|
|
compression_ratio: 0.1,
|
|
quantization_bits: 8,
|
|
sparsification_enabled: true,
|
|
top_k_ratio: 0.01,
|
|
},
|
|
monitoring: MonitoringConfig {
|
|
enabled: true,
|
|
metrics_interval: 30,
|
|
profiling_enabled: false,
|
|
log_level: LogLevel::Info,
|
|
},
|
|
}
|
|
}
|
|
|
|
/// Configure for maximum privacy
|
|
pub fn with_maximum_privacy(mut self) -> Self {
|
|
self.privacy = Some(PrivacyConfig::DifferentialPrivacy {
|
|
epsilon: 0.1, // Strong privacy guarantee
|
|
delta: 1e-6,
|
|
noise_mechanism: NoiseMechanism::Gaussian { sigma: 2.0 },
|
|
});
|
|
self
|
|
}
|
|
|
|
/// Configure for Byzantine fault tolerance
|
|
pub fn with_byzantine_tolerance(mut self, enabled: bool) -> Self {
|
|
self.byzantine_tolerance = enabled;
|
|
self
|
|
}
|
|
|
|
/// Configure aggregation algorithm
|
|
pub fn with_aggregation(mut self, algorithm: aggregation::AggregationConfig) -> Self {
|
|
self.aggregation = algorithm;
|
|
self
|
|
}
|
|
}
|
|
|
|
impl Client {
|
|
/// Create a new federated learning client
|
|
pub async fn new(name: String) -> Result<Self> {
|
|
Ok(Self {
|
|
id: Uuid::new_v4(),
|
|
name,
|
|
capabilities: ClientCapabilities {
|
|
compute_score: 0.5,
|
|
memory_bytes: 1024 * 1024 * 1024, // 1GB
|
|
bandwidth_mbps: 10.0,
|
|
battery_level: None,
|
|
privacy_support: vec!["differential_privacy".to_string()],
|
|
},
|
|
status: ClientStatus::Available,
|
|
data_profile: DataProfile {
|
|
sample_count: 1000,
|
|
distribution_id: None,
|
|
quality_score: 0.8,
|
|
privacy_level: PrivacyLevel::Internal,
|
|
},
|
|
endpoint: None,
|
|
last_seen: chrono::Utc::now(),
|
|
})
|
|
}
|
|
|
|
/// Update client status
|
|
pub fn update_status(&mut self, status: ClientStatus) {
|
|
self.status = status;
|
|
self.last_seen = chrono::Utc::now();
|
|
}
|
|
}
|
|
|
|
// Helper functions for creating engine components
|
|
async fn create_aggregation_engine(
|
|
config: &aggregation::AggregationConfig,
|
|
) -> Result<Box<dyn AggregationAlgorithm + Send + Sync>> {
|
|
match config {
|
|
aggregation::AggregationConfig::FedAvg {
|
|
momentum,
|
|
adaptive_learning_rate,
|
|
..
|
|
} => Ok(Box::new(
|
|
FedAvg::new(*momentum, *adaptive_learning_rate).await?,
|
|
)),
|
|
aggregation::AggregationConfig::FedProx {
|
|
proximal_mu,
|
|
local_epochs,
|
|
..
|
|
} => Ok(Box::new(FedProx::new(*proximal_mu, *local_epochs).await?)),
|
|
aggregation::AggregationConfig::Scaffold {
|
|
learning_rate,
|
|
local_steps,
|
|
..
|
|
} => Ok(Box::new(Scaffold::new(*learning_rate, *local_steps).await?)),
|
|
aggregation::AggregationConfig::FedNova {
|
|
tau_effective,
|
|
momentum_factor,
|
|
..
|
|
} => Ok(Box::new(
|
|
FedNova::new(*tau_effective, *momentum_factor).await?,
|
|
)),
|
|
aggregation::AggregationConfig::AsyncAggregation {
|
|
staleness_threshold,
|
|
mixing_parameter,
|
|
..
|
|
} => Ok(Box::new(
|
|
AsyncAggregation::new(*staleness_threshold, *mixing_parameter).await?,
|
|
)),
|
|
}
|
|
}
|
|
|
|
async fn create_privacy_engine(
|
|
config: &PrivacyConfig,
|
|
) -> Result<Box<dyn PrivacyMechanism + Send + Sync>> {
|
|
match config {
|
|
PrivacyConfig::DifferentialPrivacy {
|
|
epsilon,
|
|
delta,
|
|
noise_mechanism: _,
|
|
} => Ok(Box::new(DifferentialPrivacy::new(*epsilon, *delta).await?)),
|
|
PrivacyConfig::LocalDifferentialPrivacy {
|
|
epsilon,
|
|
randomization_mechanism: _,
|
|
} => Ok(Box::new(LocalDifferentialPrivacy::new(*epsilon).await?)),
|
|
PrivacyConfig::SecureMultiPartyComputation {
|
|
threshold,
|
|
security_parameter,
|
|
} => Ok(Box::new(
|
|
SecureMultiPartyComputation::new(*threshold, *security_parameter).await?,
|
|
)),
|
|
PrivacyConfig::HomomorphicEncryption {
|
|
key_size,
|
|
precision,
|
|
} => Ok(Box::new(
|
|
HomomorphicEncryption::new(*key_size, *precision).await?,
|
|
)),
|
|
}
|
|
}
|
|
|
|
async fn create_byzantine_protection() -> Result<Box<dyn ByzantineRobust + Send + Sync>> {
|
|
// Default to Krum for Byzantine protection
|
|
Ok(Box::new(Krum::new(0.1).await?))
|
|
}
|
|
|
|
impl Default for FederatedConfig {
|
|
fn default() -> Self {
|
|
Self::new()
|
|
}
|
|
}
|
|
|
|
/// Version information
|
|
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
|
|
|
|
impl std::fmt::Debug for FederatedSystem {
|
|
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
|
f.debug_struct("FederatedSystem")
|
|
.field("config", &self.config)
|
|
.field("clients", &self.clients)
|
|
.field("aggregation_engine", &"Box<dyn AggregationAlgorithm>")
|
|
.field(
|
|
"privacy_engine",
|
|
&self
|
|
.privacy_engine
|
|
.as_ref()
|
|
.map(|_| "Box<dyn PrivacyMechanism>"),
|
|
)
|
|
.field(
|
|
"byzantine_protection",
|
|
&self
|
|
.byzantine_protection
|
|
.as_ref()
|
|
.map(|_| "Box<dyn ByzantineRobust>"),
|
|
)
|
|
.field("infrastructure", &"Infrastructure")
|
|
.field("metrics", &self.metrics)
|
|
.finish()
|
|
}
|
|
}
|
|
|
|
impl std::fmt::Debug for Infrastructure {
|
|
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
|
|
f.debug_struct("Infrastructure")
|
|
.field("client_manager", &"ClientManager")
|
|
.field("communication_optimizer", &"CommunicationOptimizer")
|
|
.field("fault_tolerance", &"FaultTolerance")
|
|
.field("monitoring_system", &"MonitoringSystem")
|
|
.field("resource_scheduler", &"ResourceScheduler")
|
|
.finish()
|
|
}
|
|
}
|