trustformers-mobile 0.1.1

//! Secure aggregation protocols and implementations
//!
//! This module implements secure aggregation protocols for federated learning,
//! including various secure aggregation schemes, multi-party computation
//! aggregation, and privacy-preserving model update aggregation.

use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use crate::federated_learning_v2_backup::types::*;
use trustformers_core::{Result, CoreError, Tensor};

/// Secure aggregation configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SecureAggregationConfig {
    /// Aggregation protocol to use
    pub protocol: SecureAggregationProtocol,
    /// Minimum number of participants required
    pub min_participants: u32,
    /// Maximum number of participants allowed
    pub max_participants: u32,
    /// Dropout tolerance (fraction of participants that can drop out)
    pub dropout_tolerance: f64,
    /// Use quantization for efficiency
    pub use_quantization: bool,
    /// Quantization bits
    pub quantization_bits: u8,
    /// Secure shuffling enabled
    pub secure_shuffling: bool,
    /// Verification threshold
    pub verification_threshold: f64,
}

/// Aggregation weights for participant updates
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AggregationWeights {
    /// Participant weights by ID
    pub participant_weights: HashMap<String, f64>,
    /// Total weight (sum of all participant weights)
    pub total_weight: f64,
    /// Normalization strategy
    pub normalization_strategy: WeightNormalizationStrategy,
}

/// Weight normalization strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum WeightNormalizationStrategy {
    /// No normalization
    None,
    /// Normalize to sum to 1.0
    SumToOne,
    /// Normalize by number of participants
    ByParticipantCount,
    /// Weighted by data size
    ByDataSize,
    /// Weighted by update quality
    ByUpdateQuality,
}

/// Secure aggregator for federated learning
#[derive(Debug)]
pub struct SecureAggregator {
    config: SecureAggregationConfig,
    participant_updates: HashMap<String, Vec<u8>>,
    participant_masks: HashMap<String, Vec<u8>>,
    aggregation_state: AggregationState,
}

/// Aggregation state tracking
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AggregationState {
    /// Waiting for participant updates
    WaitingForUpdates,
    /// Computing secure aggregation
    Computing,
    /// Aggregation completed
    Completed,
    /// Aggregation failed
    Failed,
}

impl SecureAggregator {
    /// Create a new secure aggregator
    pub fn new(config: &SecureAggregationConfig) -> Result<Self> {
        Ok(Self {
            config: config.clone(),
            participant_updates: HashMap::new(),
            participant_masks: HashMap::new(),
            aggregation_state: AggregationState::WaitingForUpdates,
        })
    }

    /// Add participant update to aggregation
    pub fn add_participant_update(&mut self, participant_id: String, update: Vec<u8>) -> Result<()> {
        if self.participant_updates.len() >= self.config.max_participants as usize {
            return Err(TrustformersError::InvalidConfiguration("Maximum participants exceeded".to_string()).into());
        }

        self.participant_updates.insert(participant_id, update);
        Ok(())
    }

    /// Add participant mask for secure aggregation
    pub fn add_participant_mask(&mut self, participant_id: String, mask: Vec<u8>) -> Result<()> {
        self.participant_masks.insert(participant_id, mask);
        Ok(())
    }

    /// Perform secure aggregation
    pub fn aggregate(&mut self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        if self.participant_updates.len() < self.config.min_participants as usize {
            return Err(TrustformersError::InvalidConfiguration("Insufficient participants for aggregation".to_string()).into());
        }

        self.aggregation_state = AggregationState::Computing;

        let result = match self.config.protocol {
            SecureAggregationProtocol::BasicSecureAggregation => {
                self.basic_secure_aggregation(weights)
            }
            SecureAggregationProtocol::FederatedSecureAggregation => {
                self.federated_secure_aggregation(weights)
            }
            SecureAggregationProtocol::PrivateFederatedLearning => {
                self.private_federated_learning_aggregation(weights)
            }
            SecureAggregationProtocol::SecAggPlus => {
                self.secagg_plus_aggregation(weights)
            }
            SecureAggregationProtocol::Flamingo => {
                self.flamingo_aggregation(weights)
            }
            SecureAggregationProtocol::FATE => {
                self.fate_aggregation(weights)
            }
        };

        match result {
            Ok(aggregated) => {
                self.aggregation_state = AggregationState::Completed;
                Ok(aggregated)
            }
            Err(e) => {
                self.aggregation_state = AggregationState::Failed;
                Err(e)
            }
        }
    }

    /// Basic secure aggregation implementation
    fn basic_secure_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        if self.participant_updates.is_empty() {
            return Err(TrustformersError::InvalidConfiguration("No participant updates available".to_string()).into());
        }

        // Get the first update to determine size
        let first_update = self.participant_updates.values().next()
            .ok_or_else(|| TrustformersError::other("No participant updates available".to_string()))?;
        let update_size = first_update.len();
        let mut aggregated = vec![0u8; update_size];

        // Perform weighted aggregation
        for (participant_id, update) in &self.participant_updates {
            let weight = weights.participant_weights.get(participant_id).unwrap_or(&1.0);

            if update.len() != update_size {
                return Err(TrustformersError::InvalidConfiguration("Update size mismatch".to_string()).into());
            }

            for (i, &byte) in update.iter().enumerate() {
                // Simplified aggregation (in practice, convert to floats, aggregate, then back)
                let weighted_value = (byte as f64 * weight) as u8;
                aggregated[i] = aggregated[i].saturating_add(weighted_value);
            }
        }

        // Apply normalization
        self.apply_normalization(&mut aggregated, weights)?;

        Ok(aggregated)
    }

    /// Federated secure aggregation implementation
    fn federated_secure_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        // Enhanced version with masking
        let mut aggregated = self.basic_secure_aggregation(weights)?;

        // Apply masks if available
        if !self.participant_masks.is_empty() {
            self.apply_secure_masks(&mut aggregated)?;
        }

        Ok(aggregated)
    }

    /// Private federated learning aggregation
    fn private_federated_learning_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        // More sophisticated aggregation with additional privacy guarantees
        let mut aggregated = self.federated_secure_aggregation(weights)?;

        // Apply additional privacy-preserving transformations
        self.apply_privacy_transformations(&mut aggregated)?;

        Ok(aggregated)
    }

    /// SecAgg+ protocol implementation
    fn secagg_plus_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        // Advanced secure aggregation with improved dropout resilience
        let mut aggregated = self.private_federated_learning_aggregation(weights)?;

        // Apply dropout-resilient techniques
        self.apply_dropout_resilience(&mut aggregated)?;

        Ok(aggregated)
    }

    /// Flamingo protocol implementation
    fn flamingo_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        // Flamingo protocol with secure shuffling
        let mut aggregated = self.secagg_plus_aggregation(weights)?;

        if self.config.secure_shuffling {
            self.apply_secure_shuffling(&mut aggregated)?;
        }

        Ok(aggregated)
    }

    /// FATE protocol implementation
    fn fate_aggregation(&self, weights: &AggregationWeights) -> Result<Vec<u8>> {
        // FATE (Federated AI Technology Enabler) protocol
        let mut aggregated = self.flamingo_aggregation(weights)?;

        // Apply FATE-specific optimizations
        self.apply_fate_optimizations(&mut aggregated)?;

        Ok(aggregated)
    }

    /// Apply secure masks to aggregated result
    fn apply_secure_masks(&self, aggregated: &mut [u8]) -> Result<()> {
        for mask in self.participant_masks.values() {
            if mask.len() != aggregated.len() {
                continue; // Skip mismatched masks
            }

            for (i, &mask_byte) in mask.iter().enumerate() {
                aggregated[i] ^= mask_byte; // XOR with mask
            }
        }
        Ok(())
    }

    /// Apply privacy-preserving transformations
    fn apply_privacy_transformations(&self, aggregated: &mut [u8]) -> Result<()> {
        // Add noise for differential privacy (simplified)
        for byte in aggregated.iter_mut() {
            // Simple noise addition (use proper DP noise in practice)
            let noise = 1u8; // Simplified noise
            *byte = byte.saturating_add(noise);
        }
        Ok(())
    }

    /// Apply dropout resilience techniques
    fn apply_dropout_resilience(&self, aggregated: &mut [u8]) -> Result<()> {
        // Implement dropout compensation (simplified)
        let dropout_rate = 1.0 - (self.participant_updates.len() as f64 / self.config.max_participants as f64);
        if dropout_rate > self.config.dropout_tolerance {
            // Apply compensation scaling
            for byte in aggregated.iter_mut() {
                let compensated = (*byte as f64 * (1.0 + dropout_rate)) as u8;
                *byte = compensated;
            }
        }
        Ok(())
    }

    /// Apply secure shuffling
    fn apply_secure_shuffling(&self, aggregated: &mut [u8]) -> Result<()> {
        // Simplified secure shuffling (use proper cryptographic shuffling in practice)
        let len = aggregated.len();
        for i in 0..len {
            let j = (i + 1) % len; // Simple permutation
            aggregated.swap(i, j);
        }
        Ok(())
    }

    /// Apply FATE-specific optimizations
    fn apply_fate_optimizations(&self, aggregated: &mut [u8]) -> Result<()> {
        // FATE protocol optimizations (simplified)
        if self.config.use_quantization {
            self.apply_quantization(aggregated)?;
        }
        Ok(())
    }

    /// Apply quantization for efficiency
    fn apply_quantization(&self, aggregated: &mut [u8]) -> Result<()> {
        let bits = self.config.quantization_bits as u32;
        let levels = (1u32 << bits) - 1;

        for byte in aggregated.iter_mut() {
            let quantized = (*byte as u32 * levels / 255) as u8;
            *byte = quantized;
        }
        Ok(())
    }

    /// Apply weight normalization
    fn apply_normalization(&self, aggregated: &mut [u8], weights: &AggregationWeights) -> Result<()> {
        match weights.normalization_strategy {
            WeightNormalizationStrategy::None => {
                // No normalization
            }
            WeightNormalizationStrategy::SumToOne => {
                // Normalize by total weight
                for byte in aggregated.iter_mut() {
                    let normalized = (*byte as f64 / weights.total_weight) as u8;
                    *byte = normalized;
                }
            }
            WeightNormalizationStrategy::ByParticipantCount => {
                // Normalize by participant count
                let count = self.participant_updates.len() as f64;
                for byte in aggregated.iter_mut() {
                    let normalized = (*byte as f64 / count) as u8;
                    *byte = normalized;
                }
            }
            WeightNormalizationStrategy::ByDataSize => {
                // Normalize by data size (simplified - use actual data sizes in practice)
                let avg_data_size = 1000.0; // Simplified
                for byte in aggregated.iter_mut() {
                    let normalized = (*byte as f64 / avg_data_size) as u8;
                    *byte = normalized;
                }
            }
            WeightNormalizationStrategy::ByUpdateQuality => {
                // Normalize by update quality (simplified)
                let avg_quality = 0.8; // Simplified
                for byte in aggregated.iter_mut() {
                    let normalized = (*byte as f64 / avg_quality) as u8;
                    *byte = normalized;
                }
            }
        }
        Ok(())
    }

    /// Get current aggregation state
    pub fn get_state(&self) -> AggregationState {
        self.aggregation_state
    }

    /// Get number of participating clients
    pub fn get_participant_count(&self) -> usize {
        self.participant_updates.len()
    }

    /// Verify aggregation integrity
    pub fn verify_aggregation(&self, aggregated_result: &[u8]) -> Result<bool> {
        // Simplified verification (implement proper verification in practice)
        if aggregated_result.is_empty() {
            return Ok(false);
        }

        // Check if result size matches expected size
        if let Some(first_update) = self.participant_updates.values().next() {
            if aggregated_result.len() != first_update.len() {
                return Ok(false);
            }
        }

        // Additional integrity checks can be added here
        Ok(true)
    }

    /// Reset aggregator for new round
    pub fn reset(&mut self) {
        self.participant_updates.clear();
        self.participant_masks.clear();
        self.aggregation_state = AggregationState::WaitingForUpdates;
    }
}

impl AggregationWeights {
    /// Create new aggregation weights
    pub fn new(normalization_strategy: WeightNormalizationStrategy) -> Self {
        Self {
            participant_weights: HashMap::new(),
            total_weight: 0.0,
            normalization_strategy,
        }
    }

    /// Add participant weight
    pub fn add_participant(&mut self, participant_id: String, weight: f64) {
        self.participant_weights.insert(participant_id, weight);
        self.recalculate_total_weight();
    }

    /// Update participant weight
    pub fn update_participant_weight(&mut self, participant_id: &str, weight: f64) -> Result<()> {
        if self.participant_weights.contains_key(participant_id) {
            self.participant_weights.insert(participant_id.to_string(), weight);
            self.recalculate_total_weight();
            Ok(())
        } else {
            Err(TrustformersError::InvalidConfiguration(format!("Participant {} not found", participant_id)))
        }
    }

    /// Remove participant
    pub fn remove_participant(&mut self, participant_id: &str) -> Result<()> {
        if self.participant_weights.remove(participant_id).is_some() {
            self.recalculate_total_weight();
            Ok(())
        } else {
            Err(TrustformersError::InvalidConfiguration(format!("Participant {} not found", participant_id)))
        }
    }

    /// Recalculate total weight
    fn recalculate_total_weight(&mut self) {
        self.total_weight = self.participant_weights.values().sum();
    }

    /// Get participant weight
    pub fn get_participant_weight(&self, participant_id: &str) -> Option<f64> {
        self.participant_weights.get(participant_id).copied()
    }

    /// Get normalized weights
    pub fn get_normalized_weights(&self) -> HashMap<String, f64> {
        if self.total_weight == 0.0 {
            return self.participant_weights.clone();
        }

        self.participant_weights
            .iter()
            .map(|(id, &weight)| (id.clone(), weight / self.total_weight))
            .collect()
    }
}

impl Default for SecureAggregationConfig {
    fn default() -> Self {
        Self {
            protocol: SecureAggregationProtocol::default(),
            min_participants: 2,
            max_participants: 1000,
            dropout_tolerance: 0.3,
            use_quantization: true,
            quantization_bits: 8,
            secure_shuffling: true,
            verification_threshold: 0.95,
        }
    }
}

impl Default for WeightNormalizationStrategy {
    fn default() -> Self {
        Self::SumToOne
    }
}