# Federated Learning Engine ## Privacy-Preserving Cross-Project Learning for Enhanced BMAD System The Federated Learning Engine enables secure, privacy-preserving learning across multiple projects, teams, and organizations while extracting valuable patterns and insights that benefit the entire development community. ### Federated Learning Architecture #### Privacy-Preserving Learning Framework ```yaml federated_learning_architecture: privacy_preservation: differential_privacy: - noise_injection: "Add calibrated noise to protect individual data points" - epsilon_budget: "Manage privacy budget across learning operations" - composition_tracking: "Track cumulative privacy loss" - adaptive_noise: "Adjust noise based on data sensitivity" secure_aggregation: - homomorphic_encryption: "Encrypt individual contributions" - secure_multi_party_computation: "Compute without revealing data" - federated_averaging: "Aggregate model updates securely" - byzantine_tolerance: "Handle malicious participants" data_anonymization: - k_anonymity: "Ensure minimum group sizes for anonymity" - l_diversity: "Ensure diversity in sensitive attributes" - t_closeness: "Ensure distribution similarity" - synthetic_data_generation: "Generate privacy-preserving synthetic data" access_control: - role_based_access: "Control access based on organizational roles" - attribute_based_access: "Fine-grained access control" - audit_logging: "Complete audit trail of data access" - consent_management: "Manage data usage consent" learning_domains: pattern_aggregation: - code_patterns: "Aggregate successful code patterns across projects" - architectural_patterns: "Learn architectural decisions and outcomes" - workflow_patterns: "Identify effective development workflows" - collaboration_patterns: "Understand team collaboration effectiveness" success_prediction: - project_success_factors: "Identify factors leading to project success" - technology_adoption_success: "Predict technology adoption outcomes" - team_performance_indicators: "Understand team effectiveness patterns" - timeline_accuracy_patterns: "Learn from project timeline experiences" anti_pattern_detection: - code_anti_patterns: "Identify patterns leading to technical debt" - process_anti_patterns: "Detect ineffective process patterns" - communication_anti_patterns: "Identify problematic communication patterns" - decision_anti_patterns: "Learn from poor decision outcomes" trend_analysis: - technology_trends: "Track technology adoption and success rates" - methodology_effectiveness: "Analyze development methodology outcomes" - tool_effectiveness: "Understand tool adoption and satisfaction" - skill_development_patterns: "Track team skill development paths" federation_topology: hierarchical_federation: - team_level: "Learning within individual teams" - project_level: "Learning across projects within organization" - organization_level: "Learning across organizational boundaries" - ecosystem_level: "Learning across the entire development ecosystem" peer_to_peer_federation: - direct_collaboration: "Direct learning between similar organizations" - consortium_learning: "Learning within industry consortiums" - open_source_federation: "Learning from open source contributions" - academic_partnership: "Collaboration with research institutions" ``` #### Federated Learning Implementation ```python import numpy as np import hashlib import cryptography from cryptography.fernet import Fernet import torch import torch.nn as nn from sklearn.ensemble import IsolationForest from differential_privacy import LaplaceMechanism, GaussianMechanism import asyncio import json from typing import Dict, List, Any, Optional class FederatedLearningEngine: """ Privacy-preserving federated learning system for cross-project knowledge aggregation """ def __init__(self, privacy_config=None): self.privacy_config = privacy_config or { 'epsilon': 1.0, # Differential privacy parameter 'delta': 1e-5, # Differential privacy parameter 'noise_multiplier': 1.1, 'max_grad_norm': 1.0, 'secure_aggregation': True } # Initialize privacy mechanisms self.dp_mechanism = LaplaceMechanism(epsilon=self.privacy_config['epsilon']) self.encryption_key = Fernet.generate_key() self.encryptor = Fernet(self.encryption_key) # Federation components self.federation_participants = {} self.learning_models = {} self.aggregation_server = AggregationServer(self.privacy_config) self.pattern_aggregator = PatternAggregator() # Privacy budget tracking self.privacy_budget = PrivacyBudgetTracker( total_epsilon=self.privacy_config['epsilon'], total_delta=self.privacy_config['delta'] ) async def initialize_federation(self, participant_configs): """ Initialize federated learning with multiple participants """ federation_setup = { 'federation_id': generate_uuid(), 'participants': {}, 'learning_objectives': [], 'privacy_guarantees': {}, 'aggregation_schedule': {} } # Register participants for participant_id, config in participant_configs.items(): participant = await self.register_participant(participant_id, config) federation_setup['participants'][participant_id] = participant # Define learning objectives learning_objectives = await self.define_learning_objectives(participant_configs) federation_setup['learning_objectives'] = learning_objectives # Establish privacy guarantees privacy_guarantees = await self.establish_privacy_guarantees(participant_configs) federation_setup['privacy_guarantees'] = privacy_guarantees # Setup aggregation schedule aggregation_schedule = await self.setup_aggregation_schedule(participant_configs) federation_setup['aggregation_schedule'] = aggregation_schedule return federation_setup async def register_participant(self, participant_id, config): """ Register a participant in the federated learning network """ participant = { 'id': participant_id, 'organization': config.get('organization'), 'data_characteristics': await self.analyze_participant_data(config), 'privacy_requirements': config.get('privacy_requirements', {}), 'contribution_capacity': config.get('contribution_capacity', 'medium'), 'learning_interests': config.get('learning_interests', []), 'trust_level': config.get('trust_level', 'standard'), 'encryption_key': self.generate_participant_key(participant_id) } # Validate participant eligibility eligibility = await self.validate_participant_eligibility(participant) participant['eligible'] = eligibility if eligibility['is_eligible']: self.federation_participants[participant_id] = participant # Initialize participant-specific learning models await self.initialize_participant_models(participant_id, config) return participant async def federated_pattern_learning(self, learning_round_config): """ Execute privacy-preserving pattern learning across federation """ learning_round = { 'round_id': generate_uuid(), 'config': learning_round_config, 'participant_contributions': {}, 'aggregated_patterns': {}, 'privacy_metrics': {}, 'learning_outcomes': {} } # Collect privacy-preserving contributions from participants participant_tasks = [] for participant_id in self.federation_participants: task = self.collect_participant_contribution( participant_id, learning_round_config ) participant_tasks.append(task) # Execute contribution collection in parallel participant_contributions = await asyncio.gather(*participant_tasks) # Store contributions for contribution in participant_contributions: learning_round['participant_contributions'][contribution['participant_id']] = contribution # Secure aggregation of contributions aggregated_patterns = await self.secure_pattern_aggregation( participant_contributions, learning_round_config ) learning_round['aggregated_patterns'] = aggregated_patterns # Calculate privacy metrics privacy_metrics = await self.calculate_privacy_metrics( participant_contributions, aggregated_patterns ) learning_round['privacy_metrics'] = privacy_metrics # Derive learning outcomes learning_outcomes = await self.derive_learning_outcomes( aggregated_patterns, learning_round_config ) learning_round['learning_outcomes'] = learning_outcomes # Distribute learning outcomes to participants await self.distribute_learning_outcomes( learning_outcomes, self.federation_participants ) return learning_round async def collect_participant_contribution(self, participant_id, learning_config): """ Collect privacy-preserving contribution from a participant """ participant = self.federation_participants[participant_id] contribution = { 'participant_id': participant_id, 'contribution_type': learning_config['learning_type'], 'privacy_preserved_data': {}, 'local_patterns': {}, 'aggregation_metadata': {} } # Extract local patterns with privacy preservation if learning_config['learning_type'] == 'code_patterns': local_patterns = await self.extract_privacy_preserved_code_patterns( participant_id, learning_config ) elif learning_config['learning_type'] == 'success_patterns': local_patterns = await self.extract_privacy_preserved_success_patterns( participant_id, learning_config ) elif learning_config['learning_type'] == 'anti_patterns': local_patterns = await self.extract_privacy_preserved_anti_patterns( participant_id, learning_config ) else: local_patterns = await self.extract_generic_privacy_preserved_patterns( participant_id, learning_config ) contribution['local_patterns'] = local_patterns # Apply differential privacy dp_patterns = await self.apply_differential_privacy( local_patterns, participant['privacy_requirements'] ) contribution['privacy_preserved_data'] = dp_patterns # Encrypt contribution for secure transmission encrypted_contribution = await self.encrypt_contribution( contribution, participant['encryption_key'] ) return encrypted_contribution async def extract_privacy_preserved_code_patterns(self, participant_id, learning_config): """ Extract code patterns with privacy preservation """ # Get participant's local code data local_code_data = await self.get_participant_code_data(participant_id) privacy_preserved_patterns = { 'pattern_types': {}, 'frequency_distributions': {}, 'success_correlations': {}, 'anonymized_examples': {} } # Extract pattern types with k-anonymity pattern_types = await self.extract_pattern_types_with_kanonymity( local_code_data, k=learning_config.get('k_anonymity', 5) ) privacy_preserved_patterns['pattern_types'] = pattern_types # Calculate frequency distributions with differential privacy frequency_distributions = await self.calculate_dp_frequency_distributions( local_code_data, self.privacy_config['epsilon'] / 4 # Budget allocation ) privacy_preserved_patterns['frequency_distributions'] = frequency_distributions # Analyze success correlations with privacy preservation success_correlations = await self.analyze_success_correlations_privately( local_code_data, self.privacy_config['epsilon'] / 4 # Budget allocation ) privacy_preserved_patterns['success_correlations'] = success_correlations # Generate anonymized examples anonymized_examples = await self.generate_anonymized_code_examples( local_code_data, learning_config.get('max_examples', 10) ) privacy_preserved_patterns['anonymized_examples'] = anonymized_examples return privacy_preserved_patterns async def secure_pattern_aggregation(self, participant_contributions, learning_config): """ Securely aggregate patterns from all participants """ aggregation_results = { 'global_patterns': {}, 'consensus_patterns': {}, 'divergent_patterns': {}, 'confidence_scores': {} } # Decrypt contributions decrypted_contributions = [] for contribution in participant_contributions: decrypted = await self.decrypt_contribution(contribution) decrypted_contributions.append(decrypted) # Aggregate patterns using secure multi-party computation if learning_config.get('use_secure_aggregation', True): global_patterns = await self.secure_multiparty_aggregation( decrypted_contributions ) else: global_patterns = await self.simple_aggregation( decrypted_contributions ) aggregation_results['global_patterns'] = global_patterns # Identify consensus patterns (patterns agreed upon by majority) consensus_patterns = await self.identify_consensus_patterns( decrypted_contributions, consensus_threshold=learning_config.get('consensus_threshold', 0.7) ) aggregation_results['consensus_patterns'] = consensus_patterns # Identify divergent patterns (patterns that vary significantly) divergent_patterns = await self.identify_divergent_patterns( decrypted_contributions, divergence_threshold=learning_config.get('divergence_threshold', 0.5) ) aggregation_results['divergent_patterns'] = divergent_patterns # Calculate confidence scores for aggregated patterns confidence_scores = await self.calculate_pattern_confidence_scores( global_patterns, decrypted_contributions ) aggregation_results['confidence_scores'] = confidence_scores return aggregation_results async def apply_differential_privacy(self, patterns, privacy_requirements): """ Apply differential privacy to pattern data """ epsilon = privacy_requirements.get('epsilon', self.privacy_config['epsilon']) sensitivity = privacy_requirements.get('sensitivity', 1.0) dp_patterns = {} for pattern_type, pattern_data in patterns.items(): if isinstance(pattern_data, dict): # Handle frequency counts if 'counts' in pattern_data: noisy_counts = {} for key, count in pattern_data['counts'].items(): noise = self.dp_mechanism.add_noise(count, sensitivity) noisy_counts[key] = max(0, count + noise) # Ensure non-negative dp_patterns[pattern_type] = { **pattern_data, 'counts': noisy_counts } # Handle continuous values elif 'values' in pattern_data: noisy_values = [] for value in pattern_data['values']: noise = self.dp_mechanism.add_noise(value, sensitivity) noisy_values.append(value + noise) dp_patterns[pattern_type] = { **pattern_data, 'values': noisy_values } else: # For other types, apply noise to numerical fields dp_pattern_data = {} for key, value in pattern_data.items(): if isinstance(value, (int, float)): noise = self.dp_mechanism.add_noise(value, sensitivity) dp_pattern_data[key] = value + noise else: dp_pattern_data[key] = value dp_patterns[pattern_type] = dp_pattern_data else: # Handle simple numerical values if isinstance(pattern_data, (int, float)): noise = self.dp_mechanism.add_noise(pattern_data, sensitivity) dp_patterns[pattern_type] = pattern_data + noise else: dp_patterns[pattern_type] = pattern_data return dp_patterns class PatternAggregator: """ Aggregates patterns across multiple participants while preserving privacy """ def __init__(self): self.aggregation_strategies = { 'frequency_aggregation': FrequencyAggregationStrategy(), 'weighted_aggregation': WeightedAggregationStrategy(), 'consensus_aggregation': ConsensusAggregationStrategy(), 'hierarchical_aggregation': HierarchicalAggregationStrategy() } async def aggregate_success_patterns(self, participant_patterns, aggregation_config): """ Aggregate success patterns across participants """ aggregated_success_patterns = { 'pattern_categories': {}, 'success_factors': {}, 'correlation_patterns': {}, 'predictive_patterns': {} } # Aggregate by pattern categories for participant_pattern in participant_patterns: for category, patterns in participant_pattern.get('pattern_categories', {}).items(): if category not in aggregated_success_patterns['pattern_categories']: aggregated_success_patterns['pattern_categories'][category] = [] aggregated_success_patterns['pattern_categories'][category].extend(patterns) # Identify common success factors success_factors = await self.identify_common_success_factors(participant_patterns) aggregated_success_patterns['success_factors'] = success_factors # Analyze correlation patterns correlation_patterns = await self.analyze_cross_participant_correlations( participant_patterns ) aggregated_success_patterns['correlation_patterns'] = correlation_patterns # Generate predictive patterns predictive_patterns = await self.generate_predictive_success_patterns( aggregated_success_patterns, participant_patterns ) aggregated_success_patterns['predictive_patterns'] = predictive_patterns return aggregated_success_patterns async def identify_common_success_factors(self, participant_patterns): """ Identify success factors that appear across multiple participants """ success_factor_counts = {} total_participants = len(participant_patterns) # Count occurrences of success factors for participant_pattern in participant_patterns: success_factors = participant_pattern.get('success_factors', {}) for factor, importance in success_factors.items(): if factor not in success_factor_counts: success_factor_counts[factor] = { 'count': 0, 'total_importance': 0, 'participants': [] } success_factor_counts[factor]['count'] += 1 success_factor_counts[factor]['total_importance'] += importance success_factor_counts[factor]['participants'].append( participant_pattern.get('participant_id') ) # Calculate consensus and importance scores common_success_factors = {} for factor, data in success_factor_counts.items(): consensus_score = data['count'] / total_participants average_importance = data['total_importance'] / data['count'] # Only include factors with significant consensus if consensus_score >= 0.3: # At least 30% of participants common_success_factors[factor] = { 'consensus_score': consensus_score, 'average_importance': average_importance, 'participant_count': data['count'], 'total_participants': total_participants } return common_success_factors class PrivacyBudgetTracker: """ Track and manage differential privacy budget across learning operations """ def __init__(self, total_epsilon, total_delta): self.total_epsilon = total_epsilon self.total_delta = total_delta self.used_epsilon = 0.0 self.used_delta = 0.0 self.budget_allocations = {} self.operation_history = [] async def allocate_budget(self, operation_id, requested_epsilon, requested_delta): """ Allocate privacy budget for a specific operation """ remaining_epsilon = self.total_epsilon - self.used_epsilon remaining_delta = self.total_delta - self.used_delta if requested_epsilon > remaining_epsilon or requested_delta > remaining_delta: return { 'allocation_successful': False, 'reason': 'insufficient_budget', 'remaining_epsilon': remaining_epsilon, 'remaining_delta': remaining_delta, 'requested_epsilon': requested_epsilon, 'requested_delta': requested_delta } # Allocate budget self.budget_allocations[operation_id] = { 'epsilon': requested_epsilon, 'delta': requested_delta, 'timestamp': datetime.utcnow(), 'status': 'allocated' } return { 'allocation_successful': True, 'operation_id': operation_id, 'allocated_epsilon': requested_epsilon, 'allocated_delta': requested_delta, 'remaining_epsilon': remaining_epsilon - requested_epsilon, 'remaining_delta': remaining_delta - requested_delta } async def consume_budget(self, operation_id, actual_epsilon, actual_delta): """ Consume allocated privacy budget after operation completion """ if operation_id not in self.budget_allocations: raise ValueError(f"No budget allocation found for operation {operation_id}") allocation = self.budget_allocations[operation_id] if actual_epsilon > allocation['epsilon'] or actual_delta > allocation['delta']: raise ValueError("Actual consumption exceeds allocated budget") # Update used budget self.used_epsilon += actual_epsilon self.used_delta += actual_delta # Record operation self.operation_history.append({ 'operation_id': operation_id, 'epsilon_consumed': actual_epsilon, 'delta_consumed': actual_delta, 'timestamp': datetime.utcnow() }) # Update allocation status allocation['status'] = 'consumed' allocation['actual_epsilon'] = actual_epsilon allocation['actual_delta'] = actual_delta return { 'consumption_successful': True, 'remaining_epsilon': self.total_epsilon - self.used_epsilon, 'remaining_delta': self.total_delta - self.used_delta } ``` #### Cross-Organization Learning Network ```python class CrossOrganizationLearningNetwork: """ Facilitate learning across organizational boundaries with trust and privacy controls """ def __init__(self): self.trust_network = TrustNetwork() self.reputation_system = ReputationSystem() self.governance_framework = GovernanceFramework() self.incentive_mechanism = IncentiveMechanism() async def establish_learning_consortium(self, organizations, consortium_config): """ Establish a learning consortium across organizations """ consortium = { 'consortium_id': generate_uuid(), 'organizations': {}, 'governance_rules': {}, 'learning_agreements': {}, 'trust_relationships': {}, 'incentive_structure': {} } # Validate and register organizations for org_id, org_config in organizations.items(): org_validation = await self.validate_organization(org_id, org_config) if org_validation['is_valid']: consortium['organizations'][org_id] = org_validation # Establish governance rules governance_rules = await self.establish_governance_rules( consortium['organizations'], consortium_config ) consortium['governance_rules'] = governance_rules # Create learning agreements learning_agreements = await self.create_learning_agreements( consortium['organizations'], consortium_config ) consortium['learning_agreements'] = learning_agreements # Build trust relationships trust_relationships = await self.build_trust_relationships( consortium['organizations'] ) consortium['trust_relationships'] = trust_relationships # Design incentive structure incentive_structure = await self.design_incentive_structure( consortium['organizations'], consortium_config ) consortium['incentive_structure'] = incentive_structure return consortium async def execute_consortium_learning(self, consortium, learning_objectives): """ Execute federated learning across consortium organizations """ learning_session = { 'session_id': generate_uuid(), 'consortium_id': consortium['consortium_id'], 'objectives': learning_objectives, 'participants': {}, 'learning_outcomes': {}, 'trust_metrics': {}, 'incentive_distributions': {} } # Prepare participants for learning for org_id in consortium['organizations']: participant_prep = await self.prepare_organization_for_learning( org_id, learning_objectives, consortium['governance_rules'] ) learning_session['participants'][org_id] = participant_prep # Execute federated learning with privacy preservation learning_engine = FederatedLearningEngine( privacy_config=consortium['governance_rules']['privacy_config'] ) learning_results = await learning_engine.federated_pattern_learning({ 'learning_type': learning_objectives['type'], 'privacy_requirements': consortium['governance_rules']['privacy_requirements'], 'consensus_threshold': consortium['governance_rules']['consensus_threshold'], 'participants': learning_session['participants'] }) learning_session['learning_outcomes'] = learning_results # Update trust metrics trust_metrics = await self.update_trust_metrics( consortium, learning_results ) learning_session['trust_metrics'] = trust_metrics # Distribute incentives incentive_distributions = await self.distribute_incentives( consortium, learning_results, learning_session['participants'] ) learning_session['incentive_distributions'] = incentive_distributions return learning_session ``` ### Cross-Project Learning Commands ```bash # Federation setup and management bmad federation create --participants "org1,org2,org3" --privacy-level "high" bmad federation join --consortium-id "uuid" --organization "my-org" bmad federation status --show-participants --trust-levels # Privacy-preserving learning bmad learn patterns --cross-project --privacy-budget "epsilon=1.0,delta=1e-5" bmad learn success-factors --anonymous --min-participants 5 bmad learn anti-patterns --federated --consensus-threshold 0.7 # Trust and reputation management bmad trust analyze --organization "org-id" --reputation-metrics bmad reputation update --participant "org-id" --contribution-quality 0.9 bmad governance review --consortium-rules --compliance-check # Learning outcomes and insights bmad insights patterns --global --confidence-threshold 0.8 bmad insights trends --technology-adoption --time-window "1-year" bmad insights export --learning-outcomes --privacy-preserved ``` This Federated Learning Engine enables secure, privacy-preserving learning across projects and organizations while extracting valuable insights that benefit the entire development community. The system maintains strong privacy guarantees while enabling collaborative learning at scale.