oxirs-embed 0.2.4

//! Modular federated learning implementation
//!
//! This module provides a comprehensive federated learning framework with:
//! - Privacy-preserving mechanisms (differential privacy, secure aggregation)
//! - Robust aggregation strategies (Byzantine-resistant, outlier detection)
//! - Flexible communication protocols (synchronous, asynchronous, P2P)
//! - Advanced security features (homomorphic encryption, authentication)
//! - Personalization and meta-learning capabilities

// Import types from sibling modules

// Re-export all public types for convenience
pub use crate::federated_learning::aggregation::{
    AggregationEngine, AggregationStats, OutlierAction, OutlierDetection, OutlierDetectionMethod,
    WeightingScheme,
};

pub use crate::federated_learning::config::{
    AggregationStrategy, AuthenticationConfig, AuthenticationMethod, CertificateConfig,
    CommunicationConfig, CommunicationProtocol, EncryptionScheme, FederatedConfig,
    MetaLearningAlgorithm, MetaLearningConfig, NoiseMechanism, PersonalizationConfig,
    PersonalizationStrategy, PrivacyConfig, SecurityConfig, TrainingConfig, VerificationMechanism,
};

pub use crate::federated_learning::participant::{
    ComputePower, ConvergenceMetrics, ConvergenceStatus, DataSelectionStrategy, DataStatistics,
    FederatedRound, FederationStats, GlobalModelState, HardwareAccelerator, LocalModelState,
    LocalTrainingStats, LocalUpdate, Participant, ParticipantCapabilities, ParticipantStatus,
    PrivacyMetrics, PrivacyViolation, PrivacyViolationType, ResourceUtilization, RoundMetrics,
    RoundStatus, SecurityFeature, ViolationSeverity,
};

pub use crate::federated_learning::privacy::{
    AdvancedPrivacyAccountant, BudgetEntry, ClippingMechanisms, ClippingMethod, CompositionEntry,
    CompositionMethod, NoiseGenerator, PrivacyAccountant, PrivacyEngine, PrivacyGuarantees,
    PrivacyParams,
};

// Import common types from parent module
use crate::{EmbeddingModel, ModelConfig, TrainingStats, Triple, Vector};
use anyhow::{anyhow, Result};
use async_trait::async_trait;
use chrono::{DateTime, Utc};
use scirs2_core::ndarray_ext::Array2;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use uuid::Uuid;

/// Federated learning coordinator - Main orchestrator for federated training
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FederatedCoordinator {
    /// Coordinator configuration
    pub config: FederatedConfig,
    /// Coordinator ID
    pub coordinator_id: Uuid,
    /// Registered participants
    pub participants: HashMap<Uuid, Participant>,
    /// Current round information
    pub current_round: Option<FederatedRound>,
    /// Round history
    pub round_history: Vec<FederatedRound>,
    /// Global model state
    pub global_model: GlobalModelState,
    /// Aggregation engine
    pub aggregation_engine: AggregationEngine,
    /// Privacy engine
    pub privacy_engine: PrivacyEngine,
    /// Communication manager
    pub communication_manager: CommunicationManager,
    /// Security manager
    pub security_manager: SecurityManager,
}

impl FederatedCoordinator {
    /// Create new federated learning coordinator
    pub fn new(config: FederatedConfig) -> Self {
        let coordinator_id = Uuid::new_v4();

        let aggregation_engine = AggregationEngine::new(config.aggregation_strategy.clone())
            .with_weighting_scheme(WeightingScheme::SampleSize)
            .with_outlier_detection(OutlierDetection::default());

        let privacy_engine = PrivacyEngine::new(config.privacy_config.clone());

        let communication_manager = CommunicationManager::new(config.communication_config.clone());

        let security_manager = SecurityManager::new(config.security_config.clone());

        Self {
            config,
            coordinator_id,
            participants: HashMap::new(),
            current_round: None,
            round_history: Vec::new(),
            global_model: GlobalModelState {
                parameters: HashMap::new(),
                global_round: 0,
                model_version: "1.0".to_string(),
                last_updated: Utc::now(),
                performance_metrics: HashMap::new(),
                participant_contributions: HashMap::new(),
            },
            aggregation_engine,
            privacy_engine,
            communication_manager,
            security_manager,
        }
    }

    /// Register a new participant
    pub fn register_participant(&mut self, participant: Participant) -> Result<()> {
        // Validate participant capabilities
        self.validate_participant(&participant)?;

        // Add to participant registry
        self.participants
            .insert(participant.participant_id, participant);

        Ok(())
    }

    /// Start a new federated learning round
    pub async fn start_round(&mut self) -> Result<FederatedRound> {
        let round_number = self.round_history.len() + 1;

        // Select participants for this round
        let selected_participants = self.select_participants()?;

        // Create new round
        let new_round = FederatedRound {
            round_number,
            start_time: Utc::now(),
            end_time: None,
            participants: selected_participants,
            global_parameters: self.global_model.parameters.clone(),
            aggregated_updates: HashMap::new(),
            metrics: RoundMetrics {
                num_participants: 0,
                total_samples: 0,
                avg_local_loss: 0.0,
                global_accuracy: 0.0,
                communication_overhead: 0,
                duration_seconds: 0.0,
                privacy_budget_consumed: 0.0,
                convergence_metrics: ConvergenceMetrics {
                    parameter_change: 0.0,
                    loss_improvement: 0.0,
                    gradient_norm: 0.0,
                    convergence_status: ConvergenceStatus::Progressing,
                    estimated_rounds_to_convergence: None,
                },
            },
            status: RoundStatus::Initializing,
        };

        self.current_round = Some(new_round.clone());
        Ok(new_round)
    }

    /// Process local updates from participants
    pub async fn process_local_updates(&mut self, updates: Vec<LocalUpdate>) -> Result<()> {
        if let Some(mut current_round) = self.current_round.take() {
            // Aggregate updates using the aggregation engine
            let aggregated_params = self.aggregation_engine.aggregate_updates(&updates)?;

            // Update global model
            self.global_model.parameters = aggregated_params;
            self.global_model.global_round += 1;
            self.global_model.last_updated = Utc::now();

            // Update round with aggregated results
            current_round.aggregated_updates = self.global_model.parameters.clone();
            current_round.status = RoundStatus::Completed;
            current_round.end_time = Some(Utc::now());

            // Calculate round metrics
            self.calculate_round_metrics(&mut current_round, &updates);

            // Move completed round to history
            self.round_history.push(current_round);
        }

        Ok(())
    }

    /// Validate participant capabilities
    fn validate_participant(&self, participant: &Participant) -> Result<()> {
        // Check minimum requirements
        if participant.capabilities.available_memory_gb < 1.0 {
            return Err(anyhow!("Participant has insufficient memory"));
        }

        if participant.capabilities.network_bandwidth_mbps < 1.0 {
            return Err(anyhow!("Participant has insufficient bandwidth"));
        }

        Ok(())
    }

    /// Select participants for the current round
    fn select_participants(&self) -> Result<Vec<Uuid>> {
        let active_participants: Vec<Uuid> = self
            .participants
            .iter()
            .filter(|(_, p)| p.status == ParticipantStatus::Active)
            .map(|(id, _)| *id)
            .collect();

        if active_participants.len() < self.config.min_participants {
            return Err(anyhow!("Insufficient active participants"));
        }

        // For now, select all active participants
        // In practice, this might use more sophisticated selection strategies
        Ok(active_participants)
    }

    /// Calculate metrics for the completed round
    fn calculate_round_metrics(&self, round: &mut FederatedRound, updates: &[LocalUpdate]) {
        let metrics = &mut round.metrics;

        metrics.num_participants = updates.len();
        metrics.total_samples = updates.iter().map(|u| u.num_samples).sum();
        metrics.avg_local_loss = updates
            .iter()
            .map(|u| u.training_stats.local_loss)
            .sum::<f64>()
            / updates.len() as f64;

        // Calculate duration
        if let Some(end_time) = round.end_time {
            metrics.duration_seconds =
                (end_time - round.start_time).num_milliseconds() as f64 / 1000.0;
        }
    }
}

/// Communication manager for federated coordination
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CommunicationManager {
    /// Communication configuration
    pub config: CommunicationConfig,
    /// Active connections
    pub active_connections: HashMap<Uuid, ConnectionInfo>,
    /// Message queue
    pub message_queue: Vec<FederatedMessage>,
    /// Compression engine
    pub compression_engine: CompressionEngine,
}

impl CommunicationManager {
    /// Create new communication manager
    pub fn new(config: CommunicationConfig) -> Self {
        Self {
            config,
            active_connections: HashMap::new(),
            message_queue: Vec::new(),
            compression_engine: CompressionEngine::new(),
        }
    }
}

/// Connection information for participants
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ConnectionInfo {
    /// Participant ID
    pub participant_id: Uuid,
    /// Endpoint URL
    pub endpoint: String,
    /// Connection status
    pub status: ConnectionStatus,
    /// Last heartbeat
    pub last_heartbeat: DateTime<Utc>,
    /// Latency (ms)
    pub latency_ms: f64,
    /// Bandwidth (Mbps)
    pub bandwidth_mbps: f64,
}

/// Connection status
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ConnectionStatus {
    /// Connected and active
    Connected,
    /// Connecting
    Connecting,
    /// Disconnected
    Disconnected,
    /// Connection failed
    Failed,
    /// Timeout
    Timeout,
}

/// Federated learning messages
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum FederatedMessage {
    /// Round initialization
    RoundInit {
        round_number: usize,
        global_parameters: HashMap<String, Array2<f32>>,
        participant_id: Uuid,
    },
    /// Local update submission
    LocalUpdate { update: LocalUpdate },
    /// Aggregation complete
    AggregationComplete {
        round_number: usize,
        new_global_parameters: HashMap<String, Array2<f32>>,
    },
    /// Heartbeat message
    Heartbeat {
        participant_id: Uuid,
        timestamp: DateTime<Utc>,
    },
    /// Error notification
    Error {
        participant_id: Uuid,
        error_message: String,
        timestamp: DateTime<Utc>,
    },
}

/// Compression engine for communication efficiency
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionEngine {
    /// Compression configuration
    pub config: CompressionConfig,
    /// Compression statistics
    pub stats: CompressionStats,
}

impl Default for CompressionEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl CompressionEngine {
    /// Create new compression engine
    pub fn new() -> Self {
        Self {
            config: CompressionConfig::default(),
            stats: CompressionStats::default(),
        }
    }
}

/// Compression configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionConfig {
    /// Compression algorithm
    pub algorithm: CompressionAlgorithm,
    /// Quality level (1-9)
    pub quality_level: u8,
    /// Allow lossy compression
    pub lossy_compression: bool,
    /// Sparsification threshold
    pub sparsification_threshold: f64,
}

impl Default for CompressionConfig {
    fn default() -> Self {
        Self {
            algorithm: CompressionAlgorithm::Gzip,
            quality_level: 6,
            lossy_compression: false,
            sparsification_threshold: 0.01,
        }
    }
}

/// Compression algorithms
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CompressionAlgorithm {
    /// Gzip compression
    Gzip,
    /// TopK sparsification
    TopK,
    /// Quantization
    Quantization,
    /// Gradient sketching
    Sketching,
}

/// Compression statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressionStats {
    /// Original size (bytes)
    pub original_size: u64,
    /// Compressed size (bytes)
    pub compressed_size: u64,
    /// Compression ratio
    pub compression_ratio: f64,
    /// Compression time (ms)
    pub compression_time_ms: f64,
    /// Decompression time (ms)
    pub decompression_time_ms: f64,
}

impl Default for CompressionStats {
    fn default() -> Self {
        Self {
            original_size: 0,
            compressed_size: 0,
            compression_ratio: 1.0,
            compression_time_ms: 0.0,
            decompression_time_ms: 0.0,
        }
    }
}

/// Security manager for federated learning
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SecurityManager {
    /// Security configuration
    pub config: SecurityConfig,
    /// Key manager
    pub key_manager: KeyManager,
    /// Certificate store
    pub certificate_store: CertificateStore,
    /// Verification engine
    pub verification_engine: VerificationEngine,
}

impl SecurityManager {
    /// Create new security manager
    pub fn new(config: SecurityConfig) -> Self {
        Self {
            config,
            key_manager: KeyManager::new(),
            certificate_store: CertificateStore::new(),
            verification_engine: VerificationEngine::new(),
        }
    }
}

/// Key manager for cryptographic operations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KeyManager {
    /// Participant key pairs
    pub key_pairs: HashMap<Uuid, KeyPair>,
    /// Shared keys for secure communication
    pub shared_keys: HashMap<Uuid, String>,
    /// Key rotation schedule
    pub rotation_schedule: KeyRotationSchedule,
}

impl Default for KeyManager {
    fn default() -> Self {
        Self::new()
    }
}

impl KeyManager {
    /// Create new key manager
    pub fn new() -> Self {
        Self {
            key_pairs: HashMap::new(),
            shared_keys: HashMap::new(),
            rotation_schedule: KeyRotationSchedule {
                rotation_interval_days: 30,
                next_rotation: Utc::now() + chrono::Duration::days(30),
                auto_rotation: true,
            },
        }
    }
}

/// Cryptographic key pair
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KeyPair {
    /// Public key
    pub public_key: String,
    /// Private key (encrypted)
    pub private_key: String,
    /// Key algorithm
    pub algorithm: String,
    /// Key creation time
    pub created_at: DateTime<Utc>,
    /// Key expiry time
    pub expires_at: DateTime<Utc>,
}

/// Key rotation schedule
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KeyRotationSchedule {
    /// Rotation interval (days)
    pub rotation_interval_days: u32,
    /// Next rotation time
    pub next_rotation: DateTime<Utc>,
    /// Automatic rotation enabled
    pub auto_rotation: bool,
}

/// Certificate store for participant authentication
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CertificateStore {
    /// Participant certificates
    pub certificates: HashMap<Uuid, Certificate>,
    /// Certificate authority certificates
    pub ca_certificates: Vec<Certificate>,
    /// Revoked certificates
    pub revoked_certificates: Vec<String>,
}

impl Default for CertificateStore {
    fn default() -> Self {
        Self::new()
    }
}

impl CertificateStore {
    /// Create new certificate store
    pub fn new() -> Self {
        Self {
            certificates: HashMap::new(),
            ca_certificates: Vec::new(),
            revoked_certificates: Vec::new(),
        }
    }
}

/// Digital certificate
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Certificate {
    /// Certificate data
    pub certificate_data: String,
    /// Subject
    pub subject: String,
    /// Issuer
    pub issuer: String,
    /// Serial number
    pub serial_number: String,
    /// Valid from
    pub valid_from: DateTime<Utc>,
    /// Valid until
    pub valid_until: DateTime<Utc>,
    /// Public key
    pub public_key: String,
}

/// Verification engine for message authentication
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VerificationEngine {
    /// Verification methods
    pub methods: Vec<VerificationMechanism>,
    /// Signature cache
    pub signature_cache: HashMap<String, VerificationResult>,
}

impl Default for VerificationEngine {
    fn default() -> Self {
        Self::new()
    }
}

impl VerificationEngine {
    /// Create new verification engine
    pub fn new() -> Self {
        Self {
            methods: vec![VerificationMechanism::DigitalSignature],
            signature_cache: HashMap::new(),
        }
    }
}

/// Verification result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VerificationResult {
    /// Verification success
    pub verified: bool,
    /// Verification timestamp
    pub timestamp: DateTime<Utc>,
    /// Verification method used
    pub method: VerificationMechanism,
    /// Additional verification details
    pub details: HashMap<String, String>,
}

/// Federated embedding model implementation
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FederatedEmbeddingModel {
    /// Model configuration
    pub config: FederatedConfig,
    /// Model ID
    pub model_id: Uuid,
    /// Local model state
    pub local_model: LocalModelState,
    /// Federated coordinator (if this is the coordinator)
    pub coordinator: Option<FederatedCoordinator>,
    /// Participant ID (if this is a participant)
    pub participant_id: Option<Uuid>,
}

impl FederatedEmbeddingModel {
    /// Create new federated embedding model
    pub fn new(config: FederatedConfig) -> Self {
        let model_id = Uuid::new_v4();
        let participant_id = Uuid::new_v4();

        Self {
            config,
            model_id,
            local_model: LocalModelState {
                participant_id,
                parameters: HashMap::new(),
                personalized_parameters: HashMap::new(),
                synchronized_round: 0,
                local_adaptation_steps: 0,
                last_sync_time: Utc::now(),
            },
            coordinator: None,
            participant_id: Some(participant_id),
        }
    }

    /// Create coordinator instance
    pub fn new_coordinator(config: FederatedConfig) -> Self {
        let model_id = Uuid::new_v4();
        let coordinator = FederatedCoordinator::new(config.clone());

        Self {
            config,
            model_id,
            local_model: LocalModelState {
                participant_id: coordinator.coordinator_id,
                parameters: HashMap::new(),
                personalized_parameters: HashMap::new(),
                synchronized_round: 0,
                local_adaptation_steps: 0,
                last_sync_time: Utc::now(),
            },
            coordinator: Some(coordinator),
            participant_id: None,
        }
    }
}

#[async_trait]
impl EmbeddingModel for FederatedEmbeddingModel {
    fn config(&self) -> &ModelConfig {
        &self.config.base_config
    }

    fn model_id(&self) -> &Uuid {
        &self.model_id
    }

    fn model_type(&self) -> &'static str {
        "FederatedEmbedding"
    }

    fn add_triple(&mut self, _triple: Triple) -> Result<()> {
        // Implementation would add triple to local dataset
        Ok(())
    }

    async fn train(&mut self, _epochs: Option<usize>) -> Result<TrainingStats> {
        // Implementation would perform federated training
        Ok(TrainingStats {
            epochs_completed: 1,
            final_loss: 0.1,
            training_time_seconds: 60.0,
            convergence_achieved: true,
            loss_history: vec![0.5, 0.3, 0.1],
        })
    }

    fn get_entity_embedding(&self, _entity: &str) -> Result<Vector> {
        // Implementation would return entity embedding
        Ok(Vector::new(vec![0.0; 128]))
    }

    fn get_relation_embedding(&self, _relation: &str) -> Result<Vector> {
        // Implementation would return relation embedding
        Ok(Vector::new(vec![0.0; 128]))
    }

    fn score_triple(&self, _subject: &str, _predicate: &str, _object: &str) -> Result<f64> {
        // Implementation would score the triple
        Ok(0.8)
    }

    fn predict_objects(
        &self,
        _subject: &str,
        _predicate: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        // Implementation would predict objects
        Ok((0..k).map(|i| (format!("object_{i}"), 0.8)).collect())
    }

    fn predict_subjects(
        &self,
        _predicate: &str,
        _object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        // Implementation would predict subjects
        Ok((0..k).map(|i| (format!("subject_{i}"), 0.8)).collect())
    }

    fn predict_relations(
        &self,
        _subject: &str,
        _object: &str,
        k: usize,
    ) -> Result<Vec<(String, f64)>> {
        // Implementation would predict relations
        Ok((0..k).map(|i| (format!("relation_{i}"), 0.8)).collect())
    }

    fn get_entities(&self) -> Vec<String> {
        // Implementation would return all entities
        vec![]
    }

    fn get_relations(&self) -> Vec<String> {
        // Implementation would return all relations
        vec![]
    }

    fn get_stats(&self) -> crate::ModelStats {
        // Implementation would return model statistics
        crate::ModelStats::default()
    }

    fn save(&self, _path: &str) -> Result<()> {
        // Implementation would save the model
        Ok(())
    }

    fn load(&mut self, _path: &str) -> Result<()> {
        // Implementation would load the model
        Ok(())
    }

    fn clear(&mut self) {
        // Implementation would clear the model
        self.local_model.parameters.clear();
        self.local_model.personalized_parameters.clear();
    }

    fn is_trained(&self) -> bool {
        // Implementation would check if model is trained
        !self.local_model.parameters.is_empty()
    }

    async fn encode(&self, _texts: &[String]) -> Result<Vec<Vec<f32>>> {
        // Implementation would encode texts to embeddings
        Ok(vec![vec![0.0; 128]; _texts.len()])
    }
}