31 KiB
31 KiB
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
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
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
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
# 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.