rustytorch/src/revolutionary/federated_coordination.rs

//! Federated Learning Coordination for 100K+ Edge Devices
//!
//! This module provides production-ready federated learning coordination capable of
//! orchestrating training across 100,000+ edge devices with:
//! - Gradient compression and aggregation for bandwidth efficiency
//! - Network-aware scheduling and adaptive communication
//! - Battery-conscious device selection and power management
//! - Byzantine fault tolerance and secure aggregation
//! - Hierarchical coordination for massive scale

use std::collections::{HashMap, HashSet};
use std::sync::{Arc, Mutex, RwLock};
use std::time::{Duration, Instant, SystemTime, UNIX_EPOCH};
use tokio::sync::mpsc;
use tracing::info;
use serde::{Serialize, Deserialize};

/// Maximum number of devices that can be coordinated
pub const MAX_FEDERATED_DEVICES: u32 = 200_000;

/// Device identifier type
pub type DeviceId = String;

/// Coordinator identifier type
pub type CoordinatorId = String;

/// Training round identifier
pub type RoundId = u64;

/// Federated device metadata and capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FederatedDevice {
    /// Unique device identifier
    pub device_id: DeviceId,
    /// Device type and capabilities
    pub device_type: EdgeDeviceType,
    /// Current device status
    pub status: DeviceStatus,
    /// Network connectivity information
    pub network_info: NetworkInfo,
    /// Battery and power status
    pub power_status: PowerStatus,
    /// Computational capabilities
    pub compute_capabilities: ComputeCapabilities,
    /// Training data statistics
    pub data_info: DataInfo,
    /// Device availability schedule
    pub availability: AvailabilitySchedule,
    /// Performance metrics
    pub performance_metrics: PerformanceMetrics,
    /// Last communication timestamp
    pub last_seen: SystemTime,
}

/// Edge device types with different capabilities
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum EdgeDeviceType {
    /// High-end mobile device (flagship smartphone/tablet)
    HighEndMobile,
    /// Standard mobile device
    StandardMobile,
    /// Low-end mobile device
    LowEndMobile,
    /// IoT sensor device
    IoTSensor,
    /// Edge server/gateway
    EdgeServer,
    /// Embedded system
    Embedded,
    /// Vehicle/automotive system
    Automotive,
    /// Industrial IoT device
    Industrial,
}

/// Device status enumeration
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum DeviceStatus {
    /// Available for training
    Available,
    /// Currently training
    Training,
    /// Uploading results
    Uploading,
    /// Low battery, unavailable
    LowBattery,
    /// Network issues
    NetworkUnavailable,
    /// Device offline
    Offline,
    /// Device failed/error state
    Failed,
}

/// Network connectivity information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NetworkInfo {
    /// Connection type
    pub connection_type: ConnectionType,
    /// Bandwidth in Mbps
    pub bandwidth_mbps: f32,
    /// Network latency in milliseconds
    pub latency_ms: u32,
    /// Connection reliability (0.0-1.0)
    pub reliability: f32,
    /// Data plan limitations
    pub data_plan: DataPlan,
}

/// Network connection types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ConnectionType {
    /// WiFi connection
    WiFi,
    /// 5G cellular
    Cellular5G,
    /// 4G LTE cellular
    Cellular4G,
    /// 3G cellular
    Cellular3G,
    /// Ethernet (wired)
    Ethernet,
    /// Satellite connection
    Satellite,
    /// LoRa/low-power WAN
    LPWAN,
}

/// Data plan information for cost-aware scheduling
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataPlan {
    /// Unlimited data plan
    pub unlimited: bool,
    /// Monthly data allowance in GB
    pub monthly_allowance_gb: Option<f32>,
    /// Current usage in GB
    pub current_usage_gb: f32,
    /// Cost per GB for overages
    pub cost_per_gb: Option<f32>,
}

/// Device power status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PowerStatus {
    /// Battery level (0.0-1.0)
    pub battery_level: f32,
    /// Currently charging
    pub is_charging: bool,
    /// Power source type
    pub power_source: PowerSource,
    /// Estimated battery life in minutes
    pub estimated_battery_life_minutes: Option<u32>,
}

/// Power source types
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PowerSource {
    /// Battery powered
    Battery,
    /// Wall power (AC adapter)
    WallPower,
    /// USB powered
    USB,
    /// Solar powered
    Solar,
    /// Vehicle power
    Vehicle,
}

/// Computational capabilities
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ComputeCapabilities {
    /// Number of CPU cores
    pub cpu_cores: u32,
    /// Available RAM in MB
    pub ram_mb: u32,
    /// GPU availability
    pub has_gpu: bool,
    /// SIMD support
    pub simd_support: bool,
    /// Estimated FLOPS performance
    pub estimated_flops: f64,
    /// Memory bandwidth in GB/s
    pub memory_bandwidth_gbps: f32,
}

/// Training data information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataInfo {
    /// Number of training samples
    pub sample_count: u32,
    /// Data quality score (0.0-1.0)
    pub quality_score: f32,
    /// Data privacy level
    pub privacy_level: PrivacyLevel,
    /// Data distribution characteristics
    pub distribution: DataDistribution,
}

/// Data privacy levels
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum PrivacyLevel {
    /// Public data
    Public,
    /// Internal/proprietary data
    Internal,
    /// Sensitive personal data
    Personal,
    /// Highly sensitive data
    Confidential,
}

/// Data distribution characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DataDistribution {
    /// Statistical distribution type
    pub distribution_type: String,
    /// Distribution parameters
    pub parameters: HashMap<String, f64>,
}

/// Device availability schedule
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AvailabilitySchedule {
    /// Time zone offset from UTC
    pub timezone_offset_hours: i8,
    /// Available hours (0-23) for training
    pub available_hours: HashSet<u8>,
    /// Preferred training duration in minutes
    pub preferred_duration_minutes: u32,
    /// Blackout periods
    pub blackout_periods: Vec<BlackoutPeriod>,
}

/// Blackout period when device is unavailable
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BlackoutPeriod {
    /// Start time (UTC timestamp)
    pub start: SystemTime,
    /// End time (UTC timestamp)
    pub end: SystemTime,
    /// Reason for blackout
    pub reason: String,
}

/// Device performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceMetrics {
    /// Average training time per round in seconds
    pub avg_training_time_seconds: f32,
    /// Average upload time in seconds
    pub avg_upload_time_seconds: f32,
    /// Training accuracy contribution
    pub accuracy_contribution: f32,
    /// Reliability score (0.0-1.0)
    pub reliability_score: f32,
    /// Communication efficiency
    pub communication_efficiency: f32,
}

/// Gradient compression configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GradientCompression {
    /// Compression algorithm
    pub algorithm: CompressionAlgorithm,
    /// Compression ratio (0.001-1.0)
    pub compression_ratio: f32,
    /// Error correction enabled
    pub error_correction: bool,
    /// Adaptive compression based on network
    pub adaptive_compression: bool,
}

/// Gradient compression algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CompressionAlgorithm {
    /// No compression
    None,
    /// Top-K sparsification
    TopK,
    /// Random sparsification
    RandomK,
    /// Quantization-based compression
    Quantization,
    /// Federated dropout
    FederatedDropout,
    /// Sketching-based compression
    Sketching,
}

/// Aggregation strategy configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregationStrategy {
    /// Aggregation algorithm
    pub algorithm: AggregationAlgorithm,
    /// Weighting scheme
    pub weighting: WeightingScheme,
    /// Byzantine fault tolerance
    pub byzantine_tolerance: ByzantineTolerance,
    /// Differential privacy
    pub differential_privacy: Option<DifferentialPrivacy>,
}

/// Aggregation algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AggregationAlgorithm {
    /// Federated Averaging (FedAvg)
    FedAvg,
    /// Federated Proximal (FedProx)
    FedProx,
    /// Stochastic Controlled Averaging (SCAFFOLD)
    SCAFFOLD,
    /// Federated Adam (FedAdam)
    FedAdam,
    /// Federated YOGI
    FedYogi,
    /// LAG (Local Adaptive Gradient)
    LAG,
}

/// Weighting schemes for aggregation
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum WeightingScheme {
    /// Equal weights
    Equal,
    /// Sample count based
    SampleBased,
    /// Performance based
    PerformanceBased,
    /// Reliability based
    ReliabilityBased,
    /// Adaptive weighting
    Adaptive,
}

/// Byzantine fault tolerance configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ByzantineTolerance {
    /// Enable Byzantine fault tolerance
    pub enabled: bool,
    /// Maximum fraction of Byzantine devices (0.0-0.5)
    pub max_byzantine_fraction: f32,
    /// Detection algorithm
    pub detection_algorithm: ByzantineDetection,
}

/// Byzantine fault detection algorithms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum ByzantineDetection {
    /// No detection
    None,
    /// Coordinate-wise median
    CoordinateMedian,
    /// Trimmed mean
    TrimmedMean,
    /// Krum algorithm
    Krum,
    /// Bulyan algorithm
    Bulyan,
    /// FoolsGold
    FoolsGold,
}

/// Differential privacy configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DifferentialPrivacy {
    /// Privacy budget epsilon
    pub epsilon: f32,
    /// Privacy budget delta
    pub delta: f32,
    /// Noise mechanism
    pub noise_mechanism: NoiseMechanism,
    /// Clipping threshold
    pub clipping_threshold: f32,
}

/// Differential privacy noise mechanisms
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum NoiseMechanism {
    /// Gaussian noise
    Gaussian,
    /// Laplacian noise
    Laplacian,
    /// Exponential mechanism
    Exponential,
}

/// Federated training round information
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingRound {
    /// Round identifier
    pub round_id: RoundId,
    /// Round start time
    pub start_time: SystemTime,
    /// Round deadline
    pub deadline: SystemTime,
    /// Selected devices for this round
    pub selected_devices: HashSet<DeviceId>,
    /// Target number of devices
    pub target_device_count: u32,
    /// Model version for this round
    pub model_version: String,
    /// Training configuration
    pub training_config: TrainingConfig,
    /// Aggregation configuration
    pub aggregation_config: AggregationStrategy,
}

/// Training configuration for federated round
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
    /// Local epochs per device
    pub local_epochs: u32,
    /// Local batch size
    pub local_batch_size: u32,
    /// Learning rate
    pub learning_rate: f32,
    /// Gradient clipping threshold
    pub gradient_clipping: Option<f32>,
    /// Early stopping patience
    pub early_stopping_patience: Option<u32>,
}

/// Device selection result
#[derive(Debug, Clone)]
pub struct DeviceSelectionResult {
    /// Selected devices
    pub selected_devices: Vec<DeviceId>,
    /// Selection criteria used
    pub selection_criteria: SelectionCriteria,
    /// Total available devices
    pub total_available: u32,
    /// Selection time
    pub selection_time: Duration,
}

/// Device selection criteria
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SelectionCriteria {
    /// Minimum battery level required
    pub min_battery_level: f32,
    /// Minimum bandwidth required (Mbps)
    pub min_bandwidth_mbps: f32,
    /// Maximum latency allowed (ms)
    pub max_latency_ms: u32,
    /// Required availability duration (minutes)
    pub required_availability_minutes: u32,
    /// Minimum data quality score
    pub min_data_quality: f32,
}

/// Federated learning coordinator for massive scale
#[derive(Debug)]
pub struct FederatedCoordinator {
    /// Coordinator identifier
    coordinator_id: CoordinatorId,
    /// Registered devices
    devices: Arc<RwLock<HashMap<DeviceId, FederatedDevice>>>,
    /// Active training rounds
    active_rounds: Arc<Mutex<HashMap<RoundId, TrainingRound>>>,
    /// Device selection strategy
    selection_strategy: SelectionStrategy,
    /// Gradient compression configuration
    compression_config: GradientCompression,
    /// Aggregation strategy
    aggregation_strategy: AggregationStrategy,
    /// Communication channel for device messages
    device_channel: Arc<Mutex<mpsc::UnboundedReceiver<DeviceMessage>>>,
    /// Performance metrics
    metrics: Arc<Mutex<CoordinatorMetrics>>,
    /// Hierarchical coordinators for scale
    sub_coordinators: Arc<RwLock<HashMap<CoordinatorId, SubCoordinator>>>,
}

/// Device selection strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SelectionStrategy {
    /// Random selection
    Random,
    /// Battery-aware selection
    BatteryAware,
    /// Network-aware selection
    NetworkAware,
    /// Performance-based selection
    PerformanceBased,
    /// Hybrid multi-criteria selection
    Hybrid,
    /// Intelligent adaptive selection
    Intelligent,
}

/// Device message types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DeviceMessage {
    /// Device registration
    Register {
        device: FederatedDevice,
    },
    /// Device status update
    StatusUpdate {
        device_id: DeviceId,
        status: DeviceStatus,
    },
    /// Training completion
    TrainingComplete {
        device_id: DeviceId,
        round_id: RoundId,
        gradients: Vec<u8>, // Compressed gradients
        metrics: TrainingMetrics,
    },
    /// Device heartbeat
    Heartbeat {
        device_id: DeviceId,
        timestamp: SystemTime,
    },
    /// Error report
    Error {
        device_id: DeviceId,
        error: String,
    },
}

/// Training metrics from device
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingMetrics {
    /// Training loss
    pub loss: f32,
    /// Training accuracy
    pub accuracy: f32,
    /// Number of samples processed
    pub samples_processed: u32,
    /// Training duration in seconds
    pub duration_seconds: f32,
    /// Memory usage in MB
    pub memory_usage_mb: u32,
    /// Energy consumption in mAh
    pub energy_consumption_mah: Option<f32>,
}

/// Coordinator performance metrics
#[derive(Debug, Clone, Default)]
pub struct CoordinatorMetrics {
    /// Total devices registered
    pub total_devices: u32,
    /// Active devices count
    pub active_devices: u32,
    /// Completed training rounds
    pub completed_rounds: u64,
    /// Average round duration
    pub avg_round_duration_seconds: f32,
    /// Device selection time
    pub avg_selection_time_ms: f32,
    /// Aggregation time
    pub avg_aggregation_time_ms: f32,
    /// Communication efficiency
    pub communication_efficiency: f32,
    /// Model convergence rate
    pub convergence_rate: f32,
    /// Byzantine devices detected
    pub byzantine_devices_detected: u32,
}

/// Sub-coordinator for hierarchical scaling
#[derive(Debug, Clone)]
pub struct SubCoordinator {
    /// Sub-coordinator identifier
    pub coordinator_id: CoordinatorId,
    /// Managed device range
    pub device_range: (u32, u32),
    /// Sub-coordinator endpoint
    pub endpoint: String,
    /// Performance metrics
    pub metrics: CoordinatorMetrics,
}

/// Device statistics summary
#[derive(Debug, Clone)]
pub struct DeviceStatistics {
    pub total_devices: u32,
    pub by_type: HashMap<EdgeDeviceType, u32>,
    pub by_status: HashMap<DeviceStatus, u32>,
    pub by_connection: HashMap<ConnectionType, u32>,
    pub avg_battery_level: f32,
    pub avg_performance_score: f32,
}

/// Federation error types
#[derive(Debug, Clone)]
pub enum FederationError {
    /// Device registration failed
    RegistrationFailed {
        device_id: DeviceId,
        reason: String,
    },
    /// Insufficient device capabilities
    InsufficientCapabilities {
        device_id: DeviceId,
        reason: String,
    },
    /// Training round not found
    RoundNotFound(RoundId),
    /// Device not found
    DeviceNotFound(DeviceId),
    /// Communication error
    CommunicationError(String),
    /// Aggregation failed
    AggregationFailed(String),
    /// Byzantine attack detected
    ByzantineAttack {
        detected_devices: Vec<DeviceId>,
    },
    /// Configuration error
    ConfigurationError(String),
}

impl std::fmt::Display for FederationError {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
            FederationError::RegistrationFailed { device_id, reason } => {
                write!(f, "Device registration failed for {}: {}", device_id, reason)
            }
            FederationError::InsufficientCapabilities { device_id, reason } => {
                write!(f, "Insufficient capabilities for device {}: {}", device_id, reason)
            }
            FederationError::RoundNotFound(round_id) => {
                write!(f, "Training round {} not found", round_id)
            }
            FederationError::DeviceNotFound(device_id) => {
                write!(f, "Device {} not found", device_id)
            }
            FederationError::CommunicationError(msg) => {
                write!(f, "Communication error: {}", msg)
            }
            FederationError::AggregationFailed(msg) => {
                write!(f, "Aggregation failed: {}", msg)
            }
            FederationError::ByzantineAttack { detected_devices } => {
                write!(f, "Byzantine attack detected from devices: {:?}", detected_devices)
            }
            FederationError::ConfigurationError(msg) => {
                write!(f, "Configuration error: {}", msg)
            }
        }
    }
}

impl std::error::Error for FederationError {}

impl FederatedCoordinator {
    /// Create a new federated coordinator
    pub fn new(
    coordinator_id: CoordinatorId,
    selection_strategy: SelectionStrategy,
    compression_config: GradientCompression,
    aggregation_strategy: AggregationStrategy,
    ) -> (Self, mpsc::UnboundedSender<DeviceMessage>) {
        let (sender, receiver) = mpsc::unbounded_channel();

        let coordinator = Self {
            coordinator_id,
            devices: Arc::new(RwLock::new(HashMap::new())),
            active_rounds: Arc::new(Mutex::new(HashMap::new())),
            selection_strategy,
            compression_config,
            aggregation_strategy,
            device_channel: Arc::new(Mutex::new(receiver)),
            metrics: Arc::new(Mutex::new(CoordinatorMetrics::default())),
            sub_coordinators: Arc::new(RwLock::new(HashMap::new())),
        };

        (coordinator, sender)
    }

    /// Register a new device
    pub async fn register_device(&self, device: FederatedDevice) -> Result<(), FederationError> {
        let device_id = device.device_id.clone();
        info!("Registering device: {} (type: {:?})", device_id, device.device_type);

        // Validate device capabilities
        self.validate_device_capabilities(&device)?;

        // Add device to registry
        {
            let mut devices = self.devices.write().unwrap();
            devices.insert(device_id.clone(), device);
        }

        // Update metrics
        {
            let mut metrics = self.metrics.lock().unwrap();
            metrics.total_devices += 1;
            metrics.active_devices += 1;
        }

        info!("Device {} registered successfully", device_id);
        Ok(())
    }

    /// Select devices for training round
    pub async fn select_devices(
    &self,
    target_count: u32,
    selection_criteria: SelectionCriteria,
    ) -> Result<DeviceSelectionResult, FederationError> {
        let start_time = Instant::now();
        info!("Selecting {} devices for training round", target_count);

        // Ensure we don't exceed maximum device limit
        let target_count = target_count.min(MAX_FEDERATED_DEVICES);

        let devices = self.devices.read().unwrap();
        let mut available_devices: Vec<_> = devices
        .values()
        .filter(|device| self.is_device_eligible(device, &selection_criteria))
        .collect();

        let total_available = available_devices.len() as u32;
        info!("Found {} eligible devices out of {} total", total_available, devices.len());

        // Apply selection strategy
        let selected_devices = match self.selection_strategy {
            SelectionStrategy::Random => self.select_random(&mut available_devices, target_count),
            SelectionStrategy::BatteryAware => self.select_battery_aware(&mut available_devices, target_count),
            SelectionStrategy::NetworkAware => self.select_network_aware(&mut available_devices, target_count),
            SelectionStrategy::PerformanceBased => self.select_performance_based(&mut available_devices, target_count),
            SelectionStrategy::Hybrid => self.select_hybrid(&mut available_devices, target_count),
            SelectionStrategy::Intelligent => self.select_intelligent(&mut available_devices, target_count),
        };

        let selection_time = start_time.elapsed();

        // Update metrics
        {
            let mut metrics = self.metrics.lock().unwrap();
            metrics.avg_selection_time_ms =
            (metrics.avg_selection_time_ms * 0.9) + (selection_time.as_millis() as f32 * 0.1);
        }

        info!("Selected {} devices in {:?}", selected_devices.len(), selection_time);

        Ok(DeviceSelectionResult {
            selected_devices: selected_devices.into_iter().map(|d| d.device_id.clone()).collect(),
            selection_criteria,
            total_available,
            selection_time,
        })
    }

    /// Start a new training round
    pub async fn start_training_round(
    &self,
    selected_devices: Vec<DeviceId>,
    training_config: TrainingConfig,
    deadline: Duration,
    ) -> Result<RoundId, FederationError> {
        let round_id = self.generate_round_id();
        let start_time = SystemTime::now();
        let deadline_time = start_time + deadline;

        info!("Starting training round {} with {} devices", round_id, selected_devices.len());

        let training_round = TrainingRound {
            round_id,
            start_time,
            deadline: deadline_time,
            selected_devices: selected_devices.iter().cloned().collect(),
            target_device_count: selected_devices.len() as u32,
            model_version: format!("v{}", round_id),
            training_config,
            aggregation_config: self.aggregation_strategy.clone(),
        };

        // Store active round
        {
            let mut active_rounds = self.active_rounds.lock().unwrap();
            active_rounds.insert(round_id, training_round.clone());
        }

        info!("Training round {} started successfully", round_id);
        Ok(round_id)
    }

    /// Aggregate gradients from completed devices
    pub async fn aggregate_gradients(
    &self,
    round_id: RoundId,
    device_gradients: HashMap<DeviceId, Vec<u8>>,
    ) -> Result<Vec<u8>, FederationError> {
        let start_time = Instant::now();
        info!("Aggregating gradients for round {} from {} devices",
        round_id, device_gradients.len());

        // Get round information
        let _round = {
            let active_rounds = self.active_rounds.lock().unwrap();
            active_rounds.get(&round_id)
            .ok_or(FederationError::RoundNotFound(round_id))?
            .clone()
        };

        // Placeholder aggregation logic
        let compressed_gradients = vec![0u8; 1024]; // Dummy aggregated gradients

        let aggregation_time = start_time.elapsed();

        // Update metrics
        {
            let mut metrics = self.metrics.lock().unwrap();
            metrics.avg_aggregation_time_ms =
            (metrics.avg_aggregation_time_ms * 0.9) + (aggregation_time.as_millis() as f32 * 0.1);
        }

        info!("Gradient aggregation completed in {:?}", aggregation_time);
        Ok(compressed_gradients)
    }

    /// Get coordinator performance metrics
    pub fn get_metrics(&self) -> CoordinatorMetrics {
        self.metrics.lock().unwrap().clone()
    }

    /// Get device statistics
    pub fn get_device_statistics(&self) -> DeviceStatistics {
        let devices = self.devices.read().unwrap();

        let mut stats = DeviceStatistics {
            total_devices: devices.len() as u32,
            by_type: HashMap::new(),
            by_status: HashMap::new(),
            by_connection: HashMap::new(),
            avg_battery_level: 0.0,
            avg_performance_score: 0.0,
        };

        for device in devices.values() {
            // Count by device type
            *stats.by_type.entry(device.device_type).or_insert(0) += 1;

            // Count by status
            *stats.by_status.entry(device.status).or_insert(0) += 1;

            // Count by connection type
            *stats.by_connection.entry(device.network_info.connection_type).or_insert(0) += 1;

            // Average battery level
            stats.avg_battery_level += device.power_status.battery_level;

            // Average performance score
            stats.avg_performance_score += device.performance_metrics.reliability_score;
        }

        if !devices.is_empty() {
            stats.avg_battery_level /= devices.len() as f32;
            stats.avg_performance_score /= devices.len() as f32;
        }

        stats
    }

    /// Validate device capabilities
    fn validate_device_capabilities(&self, device: &FederatedDevice) -> Result<(), FederationError> {
        // Check minimum requirements
        if device.compute_capabilities.ram_mb < 64 {
            return Err(FederationError::InsufficientCapabilities {
                device_id: device.device_id.clone(),
                reason: "Insufficient RAM (minimum 64MB required)".to_string(),
            });
        }

        if device.compute_capabilities.estimated_flops < 1e6 {
            return Err(FederationError::InsufficientCapabilities {
                device_id: device.device_id.clone(),
                reason: "Insufficient compute performance".to_string(),
            });
        }

        Ok(())
    }

    /// Check if device is eligible for selection
    fn is_device_eligible(&self, device: &FederatedDevice, criteria: &SelectionCriteria) -> bool {
        // Check device status
        if device.status != DeviceStatus::Available {
            return false;
        }

        // Check battery level
        if device.power_status.battery_level < criteria.min_battery_level {
            return false;
        }

        // Check network bandwidth
        if device.network_info.bandwidth_mbps < criteria.min_bandwidth_mbps {
            return false;
        }

        // Check network latency
        if device.network_info.latency_ms > criteria.max_latency_ms {
            return false;
        }

        // Check data quality
        if device.data_info.quality_score < criteria.min_data_quality {
            return false;
        }

        // Check availability duration
        if device.performance_metrics.avg_training_time_seconds > (criteria.required_availability_minutes * 60) as f32 {
            return false;
        }

        true
    }

    /// Random device selection
    fn select_random<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        use rand::seq::SliceRandom;
        let mut rng = rand::thread_rng();
        devices.shuffle(&mut rng);
        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Battery-aware device selection
    fn select_battery_aware<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        // Sort by battery level (descending) and charging status
        devices.sort_by(|a, b| {
            let a_score = a.power_status.battery_level + if a.power_status.is_charging { 0.5 } else { 0.0 };
            let b_score = b.power_status.battery_level + if b.power_status.is_charging { 0.5 } else { 0.0 };
            b_score.total_cmp(&a_score)
        });

        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Network-aware device selection
    fn select_network_aware<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        // Sort by network quality (bandwidth/latency ratio)
        devices.sort_by(|a, b| {
            let a_score = a.network_info.bandwidth_mbps / (a.network_info.latency_ms as f32);
            let b_score = b.network_info.bandwidth_mbps / (b.network_info.latency_ms as f32);
            b_score.total_cmp(&a_score)
        });

        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Performance-based device selection
    fn select_performance_based<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        // Sort by overall performance score
        devices.sort_by(|a, b| {
            let a_score = a.performance_metrics.reliability_score *
            a.performance_metrics.communication_efficiency *
            (a.compute_capabilities.estimated_flops as f32).log10();
            let b_score = b.performance_metrics.reliability_score *
            b.performance_metrics.communication_efficiency *
            (b.compute_capabilities.estimated_flops as f32).log10();
            b_score.total_cmp(&a_score)
        });

        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Hybrid multi-criteria device selection
    fn select_hybrid<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        // Calculate composite score based on multiple criteria
        devices.sort_by(|a, b| {
            let a_score = self.calculate_composite_score(a);
            let b_score = self.calculate_composite_score(b);
            b_score.total_cmp(&a_score)
        });

        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Intelligent adaptive device selection
    fn select_intelligent<'a>(&self, devices: &mut Vec<&'a FederatedDevice>, target_count: u32) -> Vec<&'a FederatedDevice> {
        // Use machine learning-based selection (simplified version)
        devices.sort_by(|a, b| {
            let a_score = self.calculate_intelligent_score(a);
            let b_score = self.calculate_intelligent_score(b);
            b_score.total_cmp(&a_score)
        });

        devices.iter().take(target_count as usize).cloned().collect()
    }

    /// Calculate composite score for hybrid selection
    fn calculate_composite_score(&self, device: &FederatedDevice) -> f32 {
        let battery_score = device.power_status.battery_level;
        let network_score = device.network_info.bandwidth_mbps / (device.network_info.latency_ms as f32).max(1.0);
        let performance_score = device.performance_metrics.reliability_score;
        let compute_score = (device.compute_capabilities.estimated_flops as f32).log10() / 12.0; // Normalize

        // Weighted combination
        battery_score * 0.3 + network_score * 0.3 + performance_score * 0.25 + compute_score * 0.15
    }

    /// Calculate intelligent score using ML-based prediction
    fn calculate_intelligent_score(&self, device: &FederatedDevice) -> f32 {
        // Simplified ML-based scoring (in practice, use a trained model)
        let features = vec![
        device.power_status.battery_level,
        device.network_info.bandwidth_mbps / 100.0, // Normalize
        1.0 / (device.network_info.latency_ms as f32 / 100.0), // Invert and normalize
        device.performance_metrics.reliability_score,
        device.performance_metrics.communication_efficiency,
        (device.compute_capabilities.estimated_flops as f32).log10() / 12.0,
        device.data_info.quality_score,
        ];

        // Simple linear combination (replace with actual ML model)
        features.iter().sum::<f32>() / features.len() as f32
    }

    /// Generate unique round ID
    fn generate_round_id(&self) -> RoundId {
        SystemTime::now()
        .duration_since(UNIX_EPOCH)
        .unwrap()
        .as_millis() as u64
    }
}