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BMAD-METHOD/bmad-system/cross-project-learning/federated-learning-engine.md

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# 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.
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