//! # RTX Federated - Advanced Federated Learning Platform
//!
//! A comprehensive federated learning platform that enables privacy-preserving distributed
//! machine learning with state-of-the-art algorithms, Byzantine fault tolerance, and
//! production-grade infrastructure.
//!
//! ## Features
//!
//! ### Advanced Aggregation Algorithms
//! - **FedAvg**: Federated Averaging with momentum and adaptive learning
//! - **FedProx**: Proximal federated optimization for heterogeneous networks
//! - **SCAFFOLD**: Variance reduction with control variates for drift correction
//! - **FedNova**: Normalized averaging for non-IID data distributions
//! - **Asynchronous Aggregation**: Dynamic client participation support
//!
//! ### Personalized Federated Learning
//! - **Meta-Learning**: MAML (Model-Agnostic Meta-Learning) for federated settings
//! - **Personalization Layers**: Client-specific layers with shared backbone
//! - **Multi-Task Learning**: Joint optimization across related tasks
//! - **Client Clustering**: Data distribution-based client grouping
//! - **Transfer Learning**: Knowledge transfer across federated clients
//!
//! ### Byzantine-Robust Aggregation
//! - **Krum/Multi-Krum**: Geometric median-based Byzantine tolerance
//! - **Trimmed Mean**: Robust statistical aggregation
//! - **Gradient Clipping**: Defense against gradient attacks
//! - **Anomaly Detection**: Real-time malicious update detection
//! - **Reputation Systems**: Trust-based client weighting
//!
//! ### Privacy Mechanisms
//! - **Differential Privacy**: Gaussian and Laplace noise mechanisms
//! - **Local Differential Privacy**: Client-side privacy guarantees
//! - **Secure Multi-Party Computation**: Privacy-preserving aggregation
//! - **Homomorphic Encryption**: Computation on encrypted gradients
//! - **Privacy Accounting**: Epsilon budget management and tracking
//!
//! ### Production Infrastructure
//! - **Client Management**: Dynamic registration and lifecycle management
//! - **Communication Optimization**: Gradient compression and quantization
//! - **Fault Tolerance**: Robust recovery from client failures
//! - **Monitoring**: Real-time analytics and performance tracking
//! - **Resource-Aware Scheduling**: Adaptive client selection and scheduling
//!
//! ## Architecture
//!
//! ```text
//! ┌─────────────────────────────────────────────────────────────────────┐
//! │                         RTX Federated                               │
//! ├─────────────┬─────────────┬─────────────┬─────────────┬─────────────┤
//! │ Aggregation │ Personaliza │ Byzantine   │ Privacy     │ Infrastruct │
//! │             │ tion        │ Robust      │             │ ure         │
//! │ • FedAvg    │ • Meta-Learn│ • Krum      │ • Diff Priv │ • Client    │
//! │ • FedProx   │ • Personal  │ • Trimmed   │ • Homomorph │   Mgmt      │
//! │ • SCAFFOLD  │ • Multi-Task│   Mean      │ • SMPC      │ • Comm Opt  │
//! │ • FedNova   │ • Clustering│ • Anomaly   │ • Privacy   │ • Fault Tol │
//! │ • Async     │ • Transfer  │   Detection │   Accounting│ • Monitoring│
//! └─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘
//! ```
//!
//! ## Usage Example
//!
//! ```rust,ignore
//! use rtx_federated::{
//!     FederatedSystem, FederatedConfig, AggregationAlgorithm,
//!     PrivacyMechanism, Client, ModelUpdate
//! };
//!
//! #[tokio::main]
//! async fn main() -> Result<(), Box<dyn std::error::Error>> {
//!     // Configure federated learning system
//!     let config = FederatedConfig::new()
//!         .with_aggregation(AggregationAlgorithm::FedAvg { momentum: 0.9 })
//!         .with_privacy(PrivacyMechanism::DifferentialPrivacy { epsilon: 1.0 })
//!         .with_byzantine_tolerance(true)
//!         .with_client_selection_ratio(0.3);
//!
//!     // Initialize federated system
//!     let mut fed_system = FederatedSystem::new(config).await?;
//!
//!     // Register clients
//!     for i in 0..100 {
//!         let client = Client::new(format!("client_{}", i)).await?;
//!         fed_system.register_client(client).await?;
//!     }
//!
//!     // Run federated training rounds
//!     for round in 0..100 {
//!         let selected_clients = fed_system.select_clients().await?;
//!         let model_updates = fed_system.collect_updates(selected_clients).await?;
//!         let aggregated_model = fed_system.aggregate_updates(model_updates).await?;
//!         fed_system.distribute_model(aggregated_model).await?;
//!     }
//!
//!     Ok(())
//! }
//! ```

#![deny(unsafe_code)]
#![warn(rust_2018_idioms)]
// missing_docs is handled at workspace level

pub mod aggregation;
pub mod byzantine;
pub mod error;
pub mod infrastructure;
pub mod personalization;
pub mod privacy;
pub mod simulation;

// Re-export core types
pub use crate::{
    aggregation::{
        AggregationAlgorithm, AggregationResult, AsyncAggregation, FedAvg, FedNova, FedProx,
        ModelUpdate, Scaffold,
    },
    byzantine::{AnomalyDetector, ByzantineRobust, Krum, MultiKrum, ReputationSystem, TrimmedMean},
    error::{FederatedError, Result},
    infrastructure::{
        ClientManager, CommunicationOptimizer, FaultTolerance, MonitoringSystem, ResourceScheduler,
    },
    personalization::{
        ClientClustering, MetaLearning, MultiTaskLearning, PersonalizationLayers, TransferLearning,
    },
    privacy::{
        DifferentialPrivacy, HomomorphicEncryption, LocalDifferentialPrivacy, PrivacyBudget,
        PrivacyMechanism, SecureMultiPartyComputation,
    },
};

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
use tracing::{debug, info, warn};
use uuid::Uuid;

/// Comprehensive federated learning system
pub struct FederatedSystem {
    config: FederatedConfig,
    clients: RwLock<HashMap<Uuid, Client>>,
    aggregation_engine: Box<dyn AggregationAlgorithm + Send + Sync>,
    privacy_engine: Option<Box<dyn PrivacyMechanism + Send + Sync>>,
    byzantine_protection: Option<Box<dyn ByzantineRobust + Send + Sync>>,
    infrastructure: Infrastructure,
    metrics: RwLock<FederatedMetrics>,
}

/// Federated learning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FederatedConfig {
    /// Number of federated learning rounds
    pub num_rounds: usize,
    /// Fraction of clients selected per round
    pub client_selection_ratio: f64,
    /// Minimum number of clients required
    pub min_clients: usize,
    /// Maximum number of clients allowed
    pub max_clients: usize,
    /// Aggregation algorithm configuration
    pub aggregation: aggregation::AggregationConfig,
    /// Privacy mechanism configuration
    pub privacy: Option<PrivacyConfig>,
    /// Byzantine fault tolerance configuration
    pub byzantine_tolerance: bool,
    /// Personalization settings
    pub personalization: PersonalizationConfig,
    /// Communication optimization settings
    pub communication: CommunicationConfig,
    /// Monitoring and logging configuration
    pub monitoring: MonitoringConfig,
}

/// Aggregation algorithm configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationConfig {
    /// Federated Averaging with optional momentum
    FedAvg {
        momentum: Option<f64>,
        adaptive_learning_rate: bool,
    },
    /// Federated Proximal with regularization parameter
    FedProx { mu: f64, local_epochs: usize },
    /// SCAFFOLD with control variates
    Scaffold {
        learning_rate: f64,
        local_steps: usize,
    },
    /// FedNova with normalized averaging
    FedNova { tau_eff: f64, momentum: f64 },
    /// Asynchronous aggregation
    Async {
        staleness_threshold: usize,
        mixing_parameter: f64,
    },
}

/// Privacy mechanism configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PrivacyConfig {
    /// Differential privacy with noise parameters
    DifferentialPrivacy {
        epsilon: f64,
        delta: f64,
        noise_mechanism: NoiseMechanism,
    },
    /// Local differential privacy
    LocalDifferentialPrivacy {
        epsilon: f64,
        randomization_mechanism: RandomizationMechanism,
    },
    /// Secure multi-party computation
    SecureMultiPartyComputation {
        threshold: usize,
        security_parameter: usize,
    },
    /// Homomorphic encryption
    HomomorphicEncryption { key_size: usize, precision: usize },
}

/// Noise mechanisms for differential privacy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum NoiseMechanism {
    Gaussian { sigma: f64 },
    Laplace { scale: f64 },
    Exponential { rate: f64 },
    Discrete { sensitivity: f64 },
}

/// Randomization mechanisms for local differential privacy
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum RandomizationMechanism {
    RandomResponse,
    LocalHashing,
    Duchi,
}

/// Personalization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PersonalizationConfig {
    /// Enable meta-learning (MAML)
    pub meta_learning_enabled: bool,
    /// Number of personalization layers
    pub personalization_layers: usize,
    /// Enable multi-task learning
    pub multi_task_enabled: bool,
    /// Client clustering configuration
    pub clustering_config: Option<ClusteringConfig>,
    /// Transfer learning settings
    pub transfer_learning: TransferLearningConfig,
}

/// Client clustering configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClusteringConfig {
    /// Number of clusters
    pub num_clusters: usize,
    /// Clustering algorithm
    pub algorithm: ClusteringAlgorithm,
    /// Re-clustering frequency (rounds)
    pub reclustering_frequency: usize,
}

/// Clustering algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ClusteringAlgorithm {
    KMeans,
    SpectralClustering,
    HierarchicalClustering,
    DBScan,
}

/// Transfer learning configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransferLearningConfig {
    /// Enable transfer learning
    pub enabled: bool,
    /// Source domain similarity threshold
    pub similarity_threshold: f64,
    /// Transfer learning method
    pub method: TransferMethod,
}

/// Transfer learning methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TransferMethod {
    FineTuning,
    FeatureExtraction,
    DomainAdaptation,
    MultiTaskLearning,
}

/// Communication optimization configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CommunicationConfig {
    /// Enable gradient compression
    pub compression_enabled: bool,
    /// Compression ratio (0.0 to 1.0)
    pub compression_ratio: f64,
    /// Quantization bits
    pub quantization_bits: usize,
    /// Enable sparsification
    pub sparsification_enabled: bool,
    /// Top-k sparsification parameter
    pub top_k_ratio: f64,
}

/// Monitoring and logging configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MonitoringConfig {
    /// Enable real-time monitoring
    pub enabled: bool,
    /// Metrics collection interval (seconds)
    pub metrics_interval: u64,
    /// Enable performance profiling
    pub profiling_enabled: bool,
    /// Log verbosity level
    pub log_level: LogLevel,
}

/// Log verbosity levels
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LogLevel {
    Error,
    Warn,
    Info,
    Debug,
    Trace,
}

/// Federated learning client
#[derive(Debug, Clone)]
pub struct Client {
    /// Unique client identifier
    pub id: Uuid,
    /// Client name/label
    pub name: String,
    /// Client capabilities
    pub capabilities: ClientCapabilities,
    /// Client status
    pub status: ClientStatus,
    /// Data characteristics
    pub data_profile: DataProfile,
    /// Communication endpoint
    pub endpoint: Option<String>,
    /// Last seen timestamp
    pub last_seen: chrono::DateTime<chrono::Utc>,
}

/// Client capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ClientCapabilities {
    /// Computational power score (0.0 to 1.0)
    pub compute_score: f64,
    /// Available memory (bytes)
    pub memory_bytes: u64,
    /// Network bandwidth (Mbps)
    pub bandwidth_mbps: f64,
    /// Battery level (0.0 to 1.0, if applicable)
    pub battery_level: Option<f64>,
    /// Supported privacy mechanisms
    pub privacy_support: Vec<String>,
}

/// Client status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ClientStatus {
    Available,
    Training,
    Offline,
    Faulty,
    Malicious,
}

/// Data profile for client data characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataProfile {
    /// Number of local samples
    pub sample_count: usize,
    /// Data distribution identifier
    pub distribution_id: Option<String>,
    /// Data quality score (0.0 to 1.0)
    pub quality_score: f64,
    /// Privacy sensitivity level
    pub privacy_level: PrivacyLevel,
}

/// Privacy sensitivity levels
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum PrivacyLevel {
    Public,
    Internal,
    Confidential,
    Restricted,
    TopSecret,
}

/// Infrastructure components
pub struct Infrastructure {
    client_manager: ClientManager,
    communication_optimizer: CommunicationOptimizer,
    fault_tolerance: FaultTolerance,
    monitoring_system: MonitoringSystem,
    resource_scheduler: ResourceScheduler,
}

/// Federated learning metrics
#[derive(Debug, Default, Clone, Serialize, Deserialize)]
pub struct FederatedMetrics {
    /// Total number of rounds completed
    pub rounds_completed: usize,
    /// Current round number
    pub current_round: usize,
    /// Number of active clients
    pub active_clients: usize,
    /// Average model accuracy across clients
    pub average_accuracy: f64,
    /// Communication overhead (bytes)
    pub communication_overhead: u64,
    /// Training time per round (milliseconds)
    pub training_time_ms: u64,
    /// Privacy budget consumed
    pub privacy_budget_consumed: f64,
    /// Byzantine attacks detected
    pub byzantine_attacks_detected: usize,
    /// System uptime (seconds)
    pub uptime_seconds: u64,
}

impl FederatedSystem {
    /// Create a new federated learning system
    pub async fn new(config: FederatedConfig) -> Result<Self> {
        info!("🚀 Initializing RTX Federated Learning System...");

        // Initialize aggregation engine
        let aggregation_engine = create_aggregation_engine(&config.aggregation).await?;

        // Initialize privacy engine if configured
        let privacy_engine = if let Some(privacy_config) = &config.privacy {
            Some(create_privacy_engine(privacy_config).await?)
        } else {
            None
        };

        // Initialize Byzantine protection if enabled
        let byzantine_protection = if config.byzantine_tolerance {
            Some(create_byzantine_protection().await?)
        } else {
            None
        };

        // Initialize infrastructure
        let infrastructure = Infrastructure {
            client_manager: ClientManager::new().await?,
            communication_optimizer: CommunicationOptimizer::new(
                infrastructure::CommunicationConfig::default(),
            )
            .await?,
            fault_tolerance: FaultTolerance::new().await?,
            monitoring_system: MonitoringSystem::new(infrastructure::MonitoringConfig::default())
                .await?,
            resource_scheduler: ResourceScheduler::new().await?,
        };

        info!("✅ Federated Learning System initialized successfully");

        Ok(Self {
            config,
            clients: RwLock::new(HashMap::new()),
            aggregation_engine,
            privacy_engine,
            byzantine_protection,
            infrastructure,
            metrics: RwLock::new(FederatedMetrics::default()),
        })
    }

    /// Register a new client
    pub async fn register_client(&mut self, client: Client) -> Result<()> {
        debug!("Registering client: {}", client.name);

        let client_id = client.id;
        let mut clients = self.clients.write().await;

        if clients.len() >= self.config.max_clients {
            return Err(FederatedError::MaxClientsReached(self.config.max_clients));
        }

        clients.insert(client_id, client.clone());

        // Create ClientConnection from Client
        use infrastructure::client_manager::protocol::{ClientConnection, ConnectionQuality};
        use std::net::SocketAddr;
        use std::sync::atomic::AtomicU64;

        let connection = ClientConnection {
            client_id,
            endpoint: client
                .endpoint
                .as_ref()
                .and_then(|e| e.parse::<SocketAddr>().ok())
                .unwrap_or_else(|| "127.0.0.1:8080".parse().unwrap()),
            connected_at: chrono::Utc::now(),
            last_heartbeat: chrono::Utc::now(),
            bytes_sent: Arc::new(AtomicU64::new(0)),
            bytes_received: Arc::new(AtomicU64::new(0)),
            round_trip_time_ms: Arc::new(AtomicU64::new(0)),
            connection_quality: ConnectionQuality::default(),
            auth_token: None,
            stream: None,
        };

        self.infrastructure
            .client_manager
            .register_client(client_id, connection)
            .await?;

        let mut metrics = self.metrics.write().await;
        metrics.active_clients = clients.len();

        info!("✅ Client {} registered successfully", client_id);
        Ok(())
    }

    /// Select clients for the current training round
    pub async fn select_clients(&self) -> Result<Vec<Uuid>> {
        let clients = self.clients.read().await;
        let available_clients: Vec<Uuid> = clients
            .iter()
            .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()
    }
}