trustformers-mobile 0.1.1

//! Main federated learning engine
//!
//! This module provides the main engine that orchestrates all federated learning
//! components including privacy, cryptography, aggregation, communication,
//! security, and training coordination.

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use crate::federated_learning_v2_backup::{
    types::*,
    privacy::*,
    crypto::*,
    aggregation::*,
    communication::*,
    security::*,
    training::*,
};
use trustformers_core::{Result, , Tensor};
use trustformers_core::errors::TrustformersError;

/// Main federated learning v2.0 configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FederatedLearningV2Config {
    /// Privacy configuration
    pub privacy_config: AdvancedPrivacyConfig,
    /// Cryptographic protocols
    pub crypto_config: CryptographicConfig,
    /// Aggregation configuration
    pub aggregation_config: SecureAggregationConfig,
    /// Communication protocols
    pub communication_config: CommunicationProtocolConfig,
    /// Model training configuration
    pub training_config: FederatedTrainingConfig,
    /// Security and robustness settings
    pub security_config: SecurityConfig,
    /// Privacy accounting configuration
    pub accounting_config: PrivacyAccountingConfig,
}

/// Main federated learning v2.0 engine
#[derive(Debug)]
pub struct FederatedLearningV2Engine {
    config: FederatedLearningV2Config,
    privacy_mechanism: DifferentialPrivacyMechanism,
    privacy_accountant: PrivacyAccountant,
    crypto_manager: CryptographicManager,
    secure_aggregator: SecureAggregator,
    communication_manager: CommunicationManager,
    attack_detector: AttackDetector,
    training_coordinator: FederatedTrainingCoordinator,
    participants: HashMap<String, ParticipantInfo>,
}

/// Cryptographic manager that coordinates all crypto components
#[derive(Debug)]
pub struct CryptographicManager {
    homomorphic_encryption: HomomorphicEncryptionManager,
    secure_mpc: SecureMPCManager,
    zk_proof_manager: ZKProofManager,
    signature_manager: DigitalSignatureManager,
}

impl FederatedLearningV2Engine {
    /// Create a new federated learning v2.0 engine
    pub fn new(config: FederatedLearningV2Config) -> Result<Self> {
        // Initialize privacy components
        let privacy_mechanism = DifferentialPrivacyMechanism::new(config.privacy_config.clone());
        let privacy_accountant = PrivacyAccountant::new(&config.privacy_config, &config.accounting_config)?;

        // Initialize cryptographic components
        let crypto_manager = CryptographicManager::new(&config.crypto_config)?;

        // Initialize aggregation
        let secure_aggregator = SecureAggregator::new(&config.aggregation_config)?;

        // Initialize communication
        let communication_manager = CommunicationManager::new(config.communication_config.clone())?;

        // Initialize security
        let attack_detector = AttackDetector::new(&config.security_config)?;

        // Initialize training coordination
        let training_coordinator = FederatedTrainingCoordinator::new(config.training_config.clone());

        Ok(Self {
            config,
            privacy_mechanism,
            privacy_accountant,
            crypto_manager,
            secure_aggregator,
            communication_manager,
            attack_detector,
            training_coordinator,
            participants: HashMap::new(),
        })
    }

    /// Add a participant to the federated learning system
    pub fn add_participant(&mut self, participant: ParticipantInfo) -> Result<()> {
        // Validate participant
        self.validate_participant(&participant)?;

        // Add to training coordinator
        self.training_coordinator.add_participant(participant.clone())?;

        // Store participant info
        self.participants.insert(participant.id.clone(), participant);

        Ok(())
    }

    /// Remove a participant from the system
    pub fn remove_participant(&mut self, participant_id: &str) -> Result<()> {
        self.training_coordinator.remove_participant(participant_id)?;
        self.participants.remove(participant_id);
        Ok(())
    }

    /// Start a new training round
    pub fn start_training_round(&mut self) -> Result<Vec<String>> {
        let selected_participants = self.training_coordinator.start_round()?;

        // Broadcast round start to selected participants
        for participant_id in &selected_participants {
            let message = Message {
                id: format!("round_start_{}", self.get_current_round()),
                sender_id: "server".to_string(),
                recipient_id: participant_id.clone(),
                message_type: MessageType::RoundSync,
                payload: vec![], // Training config would go here
                timestamp: self.get_current_timestamp(),
                priority: QoSPriority::High,
            };

            self.communication_manager.send_message(message)?;
        }

        Ok(selected_participants)
    }

    /// Process participant update with full security and privacy pipeline
    pub fn process_participant_update(
        &mut self,
        participant_id: &str,
        update: Tensor,
    ) -> Result<()> {
        // Security check: analyze update for attacks
        self.attack_detector.analyze_update(participant_id, &update)?;

        // Check trust score
        let trust_score = self.attack_detector.get_trust_score(participant_id);
        if trust_score < self.config.security_config.trust_threshold {
            return Err(TrustformersError::InvalidConfiguration(
                format!("Participant {} has insufficient trust score: {}", participant_id, trust_score)
            ).into());
        }

        // Apply differential privacy
        let private_update = self.apply_differential_privacy(&update, 100)?;

        // Encrypt update if needed
        let encrypted_update = if self.config.crypto_config.homomorphic_encryption.scheme != HomomorphicScheme::TFHE {
            self.crypto_manager.encrypt_update(&private_update)?
        } else {
            self.tensor_to_bytes(&private_update)?
        };

        // Add to secure aggregator
        self.secure_aggregator.add_participant_update(participant_id.to_string(), encrypted_update)?;

        // Account for privacy budget consumption
        let (epsilon, delta) = self.compute_privacy_consumption(&update)?;
        self.privacy_accountant.account_round(epsilon, delta)?;

        Ok(())
    }

    /// Complete training round and perform secure aggregation
    pub fn complete_training_round(&mut self) -> Result<Tensor> {
        // Create aggregation weights based on trust scores
        let mut weights = AggregationWeights::new(WeightNormalizationStrategy::ByUpdateQuality);

        for (participant_id, _) in &self.participants {
            let trust_score = self.attack_detector.get_trust_score(participant_id);
            weights.add_participant(participant_id.clone(), trust_score);
        }

        // Perform secure aggregation
        let aggregated_bytes = self.secure_aggregator.aggregate(&weights)?;

        // Decrypt aggregated result if needed
        let aggregated_tensor = if self.config.crypto_config.homomorphic_encryption.scheme != HomomorphicScheme::TFHE {
            let decrypted_bytes = self.crypto_manager.decrypt_update(&aggregated_bytes)?;
            self.bytes_to_tensor(&decrypted_bytes)?
        } else {
            self.bytes_to_tensor(&aggregated_bytes)?
        };

        // Complete training round
        let participant_updates = HashMap::new(); // Simplified - would contain actual updates
        let global_model = self.training_coordinator.complete_round(participant_updates)?;

        // Reset aggregator for next round
        self.secure_aggregator.reset();

        // Broadcast global model to participants
        self.broadcast_global_model(&global_model)?;

        Ok(global_model)
    }

    /// Apply differential privacy to an update
    pub fn apply_differential_privacy(&mut self, update: &Tensor, sensitivity: f64) -> Result<Tensor> {
        let private_update = self.privacy_mechanism.apply_privacy(update, sensitivity)?;
        Ok(private_update)
    }

    /// Get current privacy budget consumption
    pub fn get_privacy_budget(&self) -> (f64, f64) {
        self.privacy_accountant.get_privacy_budget()
    }

    /// Check if privacy budget is exhausted
    pub fn is_privacy_budget_exhausted(&self) -> bool {
        self.privacy_accountant.is_budget_exhausted()
    }

    /// Generate comprehensive privacy report
    pub fn export_privacy_report(&self) -> String {
        let mut report = String::new();

        report.push_str("# Federated Learning v2.0 Privacy Report\n\n");

        // Privacy Configuration
        report.push_str("## Privacy Configuration\n");
        report.push_str(&format!("- Privacy Mechanism: {:?}\n", self.config.privacy_config.mechanism));
        report.push_str(&format!("- Privacy Model: {:?}\n", self.config.privacy_config.privacy_model));
        report.push_str(&format!("- Epsilon: {}\n", self.config.privacy_config.epsilon));
        report.push_str(&format!("- Delta: {}\n", self.config.privacy_config.delta));
        report.push_str(&format!("- Composition Method: {:?}\n", self.config.privacy_config.composition_method));
        report.push('\n');

        // Privacy Budget Status
        let (current_epsilon, current_delta) = self.get_privacy_budget();
        let (remaining_epsilon, remaining_delta) = self.privacy_accountant.get_remaining_budget();

        report.push_str("## Privacy Budget Status\n");
        report.push_str(&format!("- Current Epsilon Consumed: {:.6}\n", current_epsilon));
        report.push_str(&format!("- Current Delta Consumed: {:.6}\n", current_delta));
        report.push_str(&format!("- Remaining Epsilon: {:.6}\n", remaining_epsilon));
        report.push_str(&format!("- Remaining Delta: {:.6}\n", remaining_delta));
        report.push_str(&format!("- Budget Exhausted: {}\n", self.is_privacy_budget_exhausted()));
        report.push('\n');

        // Security Features
        report.push_str("## Security Features\n");
        report.push_str(&format!("- Attack Detection: {}\n", self.config.security_config.attack_detection_enabled));
        report.push_str(&format!("- Byzantine Fault Tolerance: {}\n", self.config.security_config.byzantine_fault_tolerance));
        report.push_str(&format!("- Trust Threshold: {}\n", self.config.security_config.trust_threshold));
        report.push_str(&format!("- Reputation System: {}\n", self.config.security_config.reputation_system));
        report.push('\n');

        // Cryptographic Protocols
        report.push_str("## Cryptographic Protocols\n");
        report.push_str(&format!("- Aggregation Protocol: {:?}\n", self.config.crypto_config.aggregation_protocol));
        report.push_str(&format!("- Homomorphic Encryption: {:?}\n", self.config.crypto_config.homomorphic_encryption.scheme));
        report.push_str(&format!("- Secure MPC Protocol: {:?}\n", self.config.crypto_config.secure_mpc.protocol));
        report.push_str(&format!("- Digital Signature: {:?}\n", self.config.crypto_config.signature_scheme));
        report.push_str(&format!("- Zero-Knowledge Proofs: {:?}\n", self.config.crypto_config.zero_knowledge_proofs.proof_system));
        report.push('\n');

        // Training Status
        report.push_str("## Training Status\n");
        let training_state = self.training_coordinator.get_training_state();
        report.push_str(&format!("- Current Round: {}\n", training_state.current_round));
        report.push_str(&format!("- Training Progress: {:?}\n", training_state.training_progress));
        report.push_str(&format!("- Current Accuracy: {:.4}\n", training_state.convergence_metrics.current_accuracy));
        report.push_str(&format!("- Best Accuracy: {:.4}\n", training_state.convergence_metrics.best_accuracy));
        report.push_str(&format!("- Current Loss: {:.4}\n", training_state.convergence_metrics.current_loss));
        report.push('\n');

        // Participant Statistics
        report.push_str("## Participant Statistics\n");
        report.push_str(&format!("- Total Participants: {}\n", self.participants.len()));

        let mut trust_scores: Vec<_> = self.participants.keys()
            .map(|id| (id, self.attack_detector.get_trust_score(id)))
            .collect();
        trust_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

        report.push_str("- Trust Scores (Top 10):\n");
        for (participant_id, trust_score) in trust_scores.iter().take(10) {
            report.push_str(&format!("  - {}: {:.4}\n", participant_id, trust_score));
        }

        report
    }

    /// Validate participant before adding
    fn validate_participant(&self, participant: &ParticipantInfo) -> Result<()> {
        if participant.id.is_empty() {
            return Err(TrustformersError::InvalidConfiguration("Participant ID cannot be empty".to_string()).into());
        }

        if participant.public_key.is_empty() {
            return Err(TrustformersError::InvalidConfiguration("Participant public key cannot be empty".to_string()).into());
        }

        if participant.trust_score < 0.0 || participant.trust_score > 1.0 {
            return Err(TrustformersError::InvalidConfiguration("Trust score must be between 0.0 and 1.0".to_string()).into());
        }

        Ok(())
    }

    /// Compute privacy consumption for an update
    fn compute_privacy_consumption(&self, _update: &Tensor) -> Result<(f64, f64)> {
        // Simplified privacy consumption computation
        let epsilon_per_round = self.config.privacy_config.epsilon / self.config.training_config.num_rounds as f64;
        let delta_per_round = self.config.privacy_config.delta / self.config.training_config.num_rounds as f64;

        Ok((epsilon_per_round, delta_per_round))
    }

    /// Broadcast global model to all participants
    fn broadcast_global_model(&mut self, model: &Tensor) -> Result<()> {
        let model_bytes = self.tensor_to_bytes(model)?;

        for participant_id in self.participants.keys() {
            let message = Message {
                id: format!("global_model_{}", self.get_current_round()),
                sender_id: "server".to_string(),
                recipient_id: participant_id.clone(),
                message_type: MessageType::AggregatedModel,
                payload: model_bytes.clone(),
                timestamp: self.get_current_timestamp(),
                priority: QoSPriority::High,
            };

            self.communication_manager.send_message(message)?;
        }

        Ok(())
    }

    /// Convert tensor to bytes (simplified)
    fn tensor_to_bytes(&self, tensor: &Tensor) -> Result<Vec<u8>> {
        let data = tensor.data()?;
        let mut bytes = Vec::new();

        for &value in data {
            bytes.extend_from_slice(&value.to_le_bytes().into());
        }

        Ok(bytes)
    }

    /// Convert bytes to tensor (simplified)
    fn bytes_to_tensor(&self, bytes: &[u8]) -> Result<Tensor> {
        let num_floats = bytes.len() / 4;
        let mut data = Vec::with_capacity(num_floats);

        for i in 0..num_floats {
            let start = i * 4;
            let end = start + 4;
            if end <= bytes.len() {
                let float_bytes = &bytes[start..end];
                let value = f32::from_le_bytes([float_bytes[0], float_bytes[1], float_bytes[2], float_bytes[3]]);
                data.push(value);
            }
        }

        Tensor::from_vec(data, &[num_floats])
    }

    /// Get current training round
    fn get_current_round(&self) -> u32 {
        self.training_coordinator.get_training_state().current_round
    }

    /// Get current timestamp
    fn get_current_timestamp(&self) -> u64 {
        std::time::SystemTime::now()
            .duration_since(std::time::UNIX_EPOCH)
            .unwrap_or_default()
            .as_secs()
    }

    /// Get training state
    pub fn get_training_state(&self) -> &FederatedTrainingState {
        self.training_coordinator.get_training_state()
    }

    /// Get detection history
    pub fn get_attack_detection_history(&self) -> &[AttackDetectionEvent] {
        self.attack_detector.get_detection_history()
    }

    /// Check if training is complete
    pub fn is_training_complete(&self) -> bool {
        matches!(
            self.training_coordinator.get_training_state().training_progress,
            TrainingProgress::Converged | TrainingProgress::Stopped | TrainingProgress::Failed
        )
    }

    /// Get best model from training
    pub fn get_best_model(&self) -> Option<&Tensor> {
        self.training_coordinator.get_best_model()
    }
}

impl CryptographicManager {
    /// Create a new cryptographic manager
    pub fn new(config: &CryptographicConfig) -> Result<Self> {
        let mut homomorphic_encryption = HomomorphicEncryptionManager::new(config.homomorphic_encryption.clone())?;
        homomorphic_encryption.generate_keys()?;

        let secure_mpc = SecureMPCManager::new(config.secure_mpc.clone(), 0)?; // Party ID 0 for server

        let mut zk_proof_manager = ZKProofManager::new(config.zero_knowledge_proofs.clone())?;
        zk_proof_manager.setup()?;

        let mut signature_manager = DigitalSignatureManager::new(config.signature_scheme)?;
        signature_manager.generate_keys()?;

        Ok(Self {
            homomorphic_encryption,
            secure_mpc,
            zk_proof_manager,
            signature_manager,
        })
    }

    /// Encrypt update using homomorphic encryption
    pub fn encrypt_update(&self, update: &Tensor) -> Result<Vec<u8>> {
        let data = update.data()?;
        self.homomorphic_encryption.encrypt(data)
    }

    /// Decrypt update using homomorphic encryption
    pub fn decrypt_update(&self, ciphertext: &[u8]) -> Result<Vec<u8>> {
        let decrypted_data = self.homomorphic_encryption.decrypt(ciphertext)?;
        let mut bytes = Vec::new();

        for value in decrypted_data {
            bytes.extend_from_slice(&value.to_le_bytes());
        }

        Ok(bytes)
    }

    /// Generate zero-knowledge proof
    pub fn generate_proof(&self, statement: &[u8], witness: &[u8]) -> Result<Vec<u8>> {
        self.zk_proof_manager.generate_proof(statement, witness)
    }

    /// Verify zero-knowledge proof
    pub fn verify_proof(&self, proof: &[u8], statement: &[u8]) -> Result<bool> {
        self.zk_proof_manager.verify_proof(proof, statement)
    }

    /// Sign message
    pub fn sign_message(&self, message: &[u8]) -> Result<Vec<u8>> {
        self.signature_manager.sign(message)
    }

    /// Verify signature
    pub fn verify_signature(&self, message: &[u8], signature: &[u8]) -> Result<bool> {
        self.signature_manager.verify(message, signature)
    }
}

impl Default for FederatedLearningV2Config {
    fn default() -> Self {
        Self {
            privacy_config: AdvancedPrivacyConfig::default(),
            crypto_config: CryptographicConfig::default(),
            aggregation_config: SecureAggregationConfig::default(),
            communication_config: CommunicationProtocolConfig::default(),
            training_config: FederatedTrainingConfig::default(),
            security_config: SecurityConfig::default(),
            accounting_config: PrivacyAccountingConfig::default(),
        }
    }
}