Add AGI module: sofia_federated.py

sofia_federated.py

@@ -0,0 +1,601 @@
+#!/usr/bin/env python3
+"""
+SOFIA Federated Learning Framework
+Implements distributed training while preserving data privacy
+"""
+
+import os
+import json
+import pickle
+import logging
+import asyncio
+import threading
+from typing import Dict, List, Tuple, Optional, Any, Callable
+from datetime import datetime
+from collections import defaultdict
+import hashlib
+import secrets
+from concurrent.futures import ThreadPoolExecutor
+import numpy as np
+
+# For demonstration - in real implementation, use proper federated learning libraries
+try:
+    import torch
+    import torch.nn as nn
+    from torch.utils.data import DataLoader, Dataset
+    TORCH_AVAILABLE = True
+except ImportError:
+    TORCH_AVAILABLE = False
+    print("Warning: PyTorch not available. Federated learning will use mock implementation.")
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class FederatedDataset(Dataset):
+    """
+    Dataset wrapper for federated learning
+    """
+
+    def __init__(self, data: List[Tuple[str, str]], tokenizer=None):
+        self.data = data
+        self.tokenizer = tokenizer
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        text1, text2 = self.data[idx]
+        if self.tokenizer:
+            # In real implementation, tokenize here
+            return {
+                'text1': text1,
+                'text2': text2,
+                'input_ids': torch.tensor([1, 2, 3]),  # Mock
+                'attention_mask': torch.tensor([1, 1, 1])  # Mock
+            }
+        return {
+            'text1': text1,
+            'text2': text2,
+            'input_ids': torch.tensor([1, 2, 3]) if TORCH_AVAILABLE else [1, 2, 3],  # Always provide input_ids
+            'attention_mask': torch.tensor([1, 1, 1]) if TORCH_AVAILABLE else [1, 1, 1]
+        }
+
+class LocalModel:
+    """
+    Represents a local model on a client device
+    """
+
+    def __init__(self, client_id: str, model_config: Dict[str, Any]):
+        self.client_id = client_id
+        self.model_config = model_config
+        self.model = None
+        self.optimizer = None
+        self.local_epochs = 1
+        self.batch_size = 32
+        self.learning_rate = 2e-5
+
+        # Privacy parameters
+        self.noise_multiplier = 0.1
+        self.max_grad_norm = 1.0
+
+        # Training state
+        self.current_round = 0
+        self.local_loss_history = []
+        self.samples_processed = 0
+
+    def initialize_model(self):
+        """Initialize the local model"""
+        if not TORCH_AVAILABLE:
+            logger.warning("PyTorch not available, using mock model")
+            self.model = MockModel()
+            return
+
+        # In real implementation, load SOFIA model architecture
+        # For now, use a simple transformer-like model
+        self.model = nn.Sequential(
+            nn.Linear(768, 512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.ReLU(),
+            nn.Linear(256, 128)
+        )
+
+        self.optimizer = torch.optim.AdamW(
+            self.model.parameters(),
+            lr=self.learning_rate,
+            weight_decay=0.01
+        )
+
+    def train_local(self, train_loader: DataLoader, epochs: int = 1) -> Dict[str, Any]:
+        """
+        Train the local model on client's data
+
+        Args:
+            train_loader: DataLoader with client's training data
+            epochs: Number of local training epochs
+
+        Returns:
+            Training statistics and model updates
+        """
+        if not self.model:
+            self.initialize_model()
+
+        self.model.train()
+        total_loss = 0
+        num_batches = 0
+
+        for epoch in range(epochs):
+            epoch_loss = 0
+            for batch in train_loader:
+                self.optimizer.zero_grad()
+
+                if TORCH_AVAILABLE:
+                    # Mock forward pass - use input_ids shape
+                    batch_size = batch['input_ids'].shape[0] if hasattr(batch['input_ids'], 'shape') else len(batch['input_ids'])
+                    outputs = self.model(torch.randn(batch_size, 768))
+                    loss = torch.nn.functional.mse_loss(outputs, torch.randn_like(outputs))
+                else:
+                    # Mock loss
+                    loss = 0.5
+
+                if TORCH_AVAILABLE and hasattr(loss, 'backward'):
+                    loss.backward()
+
+                    # Apply differential privacy (simplified)
+                    self._apply_differential_privacy()
+
+                    # Gradient clipping
+                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
+
+                    self.optimizer.step()
+
+                epoch_loss += loss.item() if TORCH_AVAILABLE and hasattr(loss, 'item') else loss
+                num_batches += 1
+                batch_size = batch['input_ids'].shape[0] if hasattr(batch['input_ids'], 'shape') else len(batch['input_ids'])
+                self.samples_processed += batch_size
+
+            avg_epoch_loss = epoch_loss / num_batches
+            self.local_loss_history.append(avg_epoch_loss)
+            total_loss += avg_epoch_loss
+
+        # Generate model update (gradient or weight differences)
+        model_update = self._generate_model_update()
+
+        training_stats = {
+            'client_id': self.client_id,
+            'round': self.current_round,
+            'epochs': epochs,
+            'avg_loss': total_loss / epochs,
+            'samples_processed': self.samples_processed,
+            'model_update_size': len(pickle.dumps(model_update))
+        }
+
+        self.current_round += 1
+        return training_stats, model_update
+
+    def _apply_differential_privacy(self):
+        """Apply differential privacy noise to gradients"""
+        if not TORCH_AVAILABLE:
+            return
+
+        for param in self.model.parameters():
+            if param.grad is not None:
+                # Add Gaussian noise for differential privacy
+                noise = torch.normal(0, self.noise_multiplier, param.grad.shape)
+                param.grad.data += noise
+
+    def _generate_model_update(self) -> Dict[str, Any]:
+        """Generate model update for aggregation"""
+        if not TORCH_AVAILABLE:
+            # Mock update
+            return {
+                'client_id': self.client_id,
+                'round': self.current_round,
+                'weights': {'layer1': np.random.randn(512, 768)},
+                'gradients': {'layer1': np.random.randn(512, 768)}
+            }
+
+        # In real implementation, compute weight differences or gradients
+        update = {
+            'client_id': self.client_id,
+            'round': self.current_round,
+            'weights': {},
+            'gradients': {}
+        }
+
+        for name, param in self.model.named_parameters():
+            update['weights'][name] = param.data.clone()
+            # In practice, you'd send gradients or weight differences
+            update['gradients'][name] = param.grad.clone() if param.grad is not None else torch.zeros_like(param)
+
+        return update
+
+    def update_model(self, global_update: Dict[str, Any]):
+        """Update local model with global aggregated update"""
+        if not TORCH_AVAILABLE:
+            logger.info(f"Client {self.client_id}: Mock model update applied")
+            return
+
+        # In real implementation, apply the global update
+        for name, param in self.model.named_parameters():
+            if name in global_update.get('weights', {}):
+                param.data = global_update['weights'][name]
+
+        logger.info(f"Client {self.client_id}: Model updated with global parameters")
+
+class MockModel:
+    """Mock model for demonstration when PyTorch is not available"""
+
+    def __init__(self):
+        self.parameters = lambda: []
+        self.training = True
+
+    def train(self):
+        self.training = True
+
+    def eval(self):
+        self.training = False
+
+    def __call__(self, x):
+        return torch.tensor(0.5) if TORCH_AVAILABLE else 0.5
+
+class FederatedAggregator:
+    """
+    Aggregates model updates from multiple clients
+    """
+
+    def __init__(self, aggregation_method: str = 'fedavg'):
+        self.aggregation_method = aggregation_method
+        self.global_model_state = {}
+        self.client_updates = []
+        self.round_number = 0
+
+        # Aggregation statistics
+        self.aggregation_history = []
+
+    def aggregate_updates(self, client_updates: List[Tuple[Dict[str, Any], Any]]) -> Dict[str, Any]:
+        """
+        Aggregate model updates from clients
+
+        Args:
+            client_updates: List of (training_stats, model_update) tuples
+
+        Returns:
+            Aggregated global model update
+        """
+        if not client_updates:
+            return {}
+
+        self.round_number += 1
+        self.client_updates = client_updates
+
+        if self.aggregation_method == 'fedavg':
+            return self._fedavg_aggregation(client_updates)
+        elif self.aggregation_method == 'fedprox':
+            return self._fedprox_aggregation(client_updates)
+        else:
+            return self._fedavg_aggregation(client_updates)
+
+    def _fedavg_aggregation(self, client_updates: List[Tuple[Dict[str, Any], Any]]) -> Dict[str, Any]:
+        """Federated Averaging aggregation"""
+        if not client_updates:
+            return {}
+
+        # Collect all model updates
+        model_updates = [update for _, update in client_updates]
+
+        # Calculate total samples for weighted averaging
+        total_samples = sum(stats['samples_processed'] for stats, _ in client_updates)
+
+        # Aggregate weights
+        aggregated_weights = {}
+        aggregated_gradients = {}
+
+        # Get all parameter names from first update
+        if model_updates:
+            param_names = set()
+            for update in model_updates:
+                if 'weights' in update:
+                    param_names.update(update['weights'].keys())
+
+            # Aggregate each parameter
+            for param_name in param_names:
+                weights = []
+                weight_contributions = []
+
+                for (stats, update) in client_updates:
+                    if param_name in update.get('weights', {}):
+                        weight = update['weights'][param_name]
+                        sample_weight = stats['samples_processed'] / total_samples
+
+                        if TORCH_AVAILABLE and isinstance(weight, torch.Tensor):
+                            weights.append(weight * sample_weight)
+                        else:
+                            weights.append(np.array(weight) * sample_weight)
+                        weight_contributions.append(sample_weight)
+
+                # Average the weights
+                if weights:
+                    if TORCH_AVAILABLE and isinstance(weights[0], torch.Tensor):
+                        aggregated_weights[param_name] = torch.stack(weights).sum(dim=0)
+                    else:
+                        aggregated_weights[param_name] = np.stack(weights).sum(axis=0)
+
+        # Store aggregation statistics
+        aggregation_stats = {
+            'round': self.round_number,
+            'num_clients': len(client_updates),
+            'total_samples': total_samples,
+            'aggregation_method': self.aggregation_method,
+            'timestamp': datetime.now().isoformat()
+        }
+
+        self.aggregation_history.append(aggregation_stats)
+
+        return {
+            'weights': aggregated_weights,
+            'gradients': aggregated_gradients,
+            'aggregation_stats': aggregation_stats
+        }
+
+    def _fedprox_aggregation(self, client_updates: List[Tuple[Dict[str, Any], Any]]) -> Dict[str, Any]:
+        """FedProx aggregation (simplified)"""
+        # FedProx adds a proximal term to prevent client drift
+        # For simplicity, using same logic as FedAvg but could add regularization
+        return self._fedavg_aggregation(client_updates)
+
+    def get_aggregation_stats(self) -> Dict[str, Any]:
+        """Get aggregation statistics"""
+        if not self.aggregation_history:
+            return {'total_rounds': 0}
+
+        recent_stats = self.aggregation_history[-1]
+        return {
+            'total_rounds': len(self.aggregation_history),
+            'last_round_clients': recent_stats['num_clients'],
+            'last_round_samples': recent_stats['total_samples'],
+            'aggregation_method': self.aggregation_method
+        }
+
+class PrivacyController:
+    """
+    Manages privacy-preserving techniques in federated learning
+    """
+
+    def __init__(self, privacy_budget: float = 1.0, delta: float = 1e-5):
+        self.privacy_budget = privacy_budget
+        self.delta = delta
+        self.spent_budget = 0.0
+
+        # Privacy mechanisms
+        self.differential_privacy_enabled = True
+        self.secure_aggregation_enabled = False
+
+    def check_privacy_budget(self, epsilon: float) -> bool:
+        """Check if privacy budget allows the operation"""
+        if self.spent_budget + epsilon > self.privacy_budget:
+            return False
+        return True
+
+    def spend_privacy_budget(self, epsilon: float):
+        """Spend privacy budget"""
+        self.spent_budget += epsilon
+
+    def apply_secure_aggregation(self, client_updates: List[Any]) -> Any:
+        """Apply secure aggregation (simplified)"""
+        if not self.secure_aggregation_enabled:
+            return client_updates
+
+        # In real implementation, use cryptographic techniques
+        # For now, just return updates (no-op)
+        logger.info("Secure aggregation applied (simplified)")
+        return client_updates
+
+    def generate_privacy_report(self) -> Dict[str, Any]:
+        """Generate privacy report"""
+        return {
+            'total_budget': self.privacy_budget,
+            'spent_budget': self.spent_budget,
+            'remaining_budget': self.privacy_budget - self.spent_budget,
+            'differential_privacy_enabled': self.differential_privacy_enabled,
+            'secure_aggregation_enabled': self.secure_aggregation_enabled,
+            'privacy_level': 'high' if self.differential_privacy_enabled else 'medium'
+        }
+
+class FederatedLearningCoordinator:
+    """
+    Coordinates federated learning across multiple clients
+    """
+
+    def __init__(self, num_clients: int = 3, rounds: int = 5):
+        self.num_clients = num_clients
+        self.rounds = rounds
+        self.clients = {}
+        self.aggregator = FederatedAggregator()
+        self.privacy_controller = PrivacyController()
+
+        # Communication
+        self.client_updates_queue = asyncio.Queue()
+        self.global_model_available = asyncio.Event()
+
+        # Statistics
+        self.training_stats = []
+        self.round_times = []
+
+    async def initialize_clients(self, client_data: Dict[str, List[Tuple[str, str]]]):
+        """Initialize federated clients with their data"""
+        for client_id, data in client_data.items():
+            model_config = {
+                'model_type': 'sofia_embedding',
+                'input_dim': 768,
+                'hidden_dim': 512,
+                'output_dim': 256
+            }
+
+            client = LocalModel(client_id, model_config)
+            client.initialize_model()
+
+            # Create dataset and dataloader
+            dataset = FederatedDataset(data)
+            dataloader = DataLoader(dataset, batch_size=client.batch_size, shuffle=True)
+
+            self.clients[client_id] = {
+                'model': client,
+                'dataloader': dataloader,
+                'data_size': len(data)
+            }
+
+        logger.info(f"Initialized {len(self.clients)} federated clients")
+
+    async def run_federated_training(self) -> Dict[str, Any]:
+        """Run federated training for specified rounds"""
+        logger.info(f"Starting federated training for {self.rounds} rounds")
+
+        for round_num in range(1, self.rounds + 1):
+            round_start = datetime.now()
+
+            logger.info(f"Round {round_num}/{self.rounds} starting")
+
+            # Client training phase
+            client_updates = await self._train_clients_in_round(round_num)
+
+            # Aggregation phase
+            global_update = self.aggregator.aggregate_updates(client_updates)
+
+            # Update all clients with global model
+            await self._update_clients_with_global_model(global_update)
+
+            # Record round statistics
+            round_time = (datetime.now() - round_start).total_seconds()
+            self.round_times.append(round_time)
+
+            round_stats = {
+                'round': round_num,
+                'num_clients': len(client_updates),
+                'round_time': round_time,
+                'aggregation_stats': global_update.get('aggregation_stats', {})
+            }
+            self.training_stats.append(round_stats)
+
+            logger.info(f"Round {round_num} completed in {round_time:.2f}s")
+
+        # Generate final report
+        final_report = self._generate_final_report()
+        return final_report
+
+    async def _train_clients_in_round(self, round_num: int) -> List[Tuple[Dict[str, Any], Any]]:
+        """Train all clients in parallel for one round"""
+        tasks = []
+
+        for client_id, client_info in self.clients.items():
+            task = asyncio.create_task(
+                self._train_single_client(client_id, client_info, round_num)
+            )
+            tasks.append(task)
+
+        # Wait for all clients to complete training
+        results = await asyncio.gather(*tasks)
+        return [r for r in results if r is not None]  # Filter out failed trainings
+
+    async def _train_single_client(self, client_id: str, client_info: Dict[str, Any], round_num: int):
+        """Train a single client"""
+        try:
+            client = client_info['model']
+            dataloader = client_info['dataloader']
+
+            # Train locally
+            training_stats, model_update = client.train_local(dataloader, epochs=client.local_epochs)
+
+            logger.info(f"Client {client_id}: Training completed - Loss: {training_stats['avg_loss']:.4f}")
+
+            return training_stats, model_update
+
+        except Exception as e:
+            logger.error(f"Client {client_id} training failed: {e}")
+            return None
+
+    async def _update_clients_with_global_model(self, global_update: Dict[str, Any]):
+        """Update all clients with the new global model"""
+        tasks = []
+
+        for client_id, client_info in self.clients.items():
+            task = asyncio.create_task(
+                self._update_single_client(client_id, client_info['model'], global_update)
+            )
+            tasks.append(task)
+
+        await asyncio.gather(*tasks)
+
+    async def _update_single_client(self, client_id: str, client_model: LocalModel, global_update: Dict[str, Any]):
+        """Update a single client with global model"""
+        try:
+            client_model.update_model(global_update)
+            logger.info(f"Client {client_id}: Model updated with global parameters")
+        except Exception as e:
+            logger.error(f"Failed to update client {client_id}: {e}")
+
+    def _generate_final_report(self) -> Dict[str, Any]:
+        """Generate final training report"""
+        total_time = sum(self.round_times)
+        avg_round_time = total_time / len(self.round_times) if self.round_times else 0
+
+        return {
+            'federated_training_completed': True,
+            'total_rounds': len(self.training_stats),
+            'total_training_time': total_time,
+            'average_round_time': avg_round_time,
+            'clients_participated': len(self.clients),
+            'privacy_report': self.privacy_controller.generate_privacy_report(),
+            'aggregation_stats': self.aggregator.get_aggregation_stats(),
+            'round_stats': self.training_stats,
+            'final_model_available': True
+        }
+
+# Example usage and testing
+async def demo_federated_learning():
+    """Demonstrate federated learning with mock data"""
+    print("SOFIA Federated Learning Demo")
+    print("=" * 40)
+
+    # Create mock client data
+    client_data = {
+        'client_1': [
+            ("Hello world", "Hi there"),
+            ("How are you", "I'm fine"),
+            ("Machine learning", "AI models")
+        ] * 10,  # Repeat for more data
+        'client_2': [
+            ("Python programming", "Code development"),
+            ("Data science", "Analytics"),
+            ("Neural networks", "Deep learning")
+        ] * 10,
+        'client_3': [
+            ("Natural language", "Text processing"),
+            ("Computer vision", "Image recognition"),
+            ("Reinforcement learning", "RL algorithms")
+        ] * 10
+    }
+
+    # Initialize coordinator
+    coordinator = FederatedLearningCoordinator(num_clients=3, rounds=3)
+
+    # Initialize clients
+    await coordinator.initialize_clients(client_data)
+
+    # Run federated training
+    print("Starting federated training...")
+    final_report = await coordinator.run_federated_training()
+
+    # Print results
+    print("\nFederated Training Results:")
+    print(f"Total rounds: {final_report['total_rounds']}")
+    print(".2f")
+    print(".2f")
+    print(f"Clients participated: {final_report['clients_participated']}")
+    print(f"Privacy level: {final_report['privacy_report']['privacy_level']}")
+
+    return final_report
+
+if __name__ == "__main__":
+    # Run demo
+    asyncio.run(demo_federated_learning())