Federated Agent Development

This document provides guidance for developing agents and workflows that operate across organizational boundaries using the Meta Agent Platform's federated collaboration features.

Overview

Federated agents enable secure, privacy-preserving workflows that span multiple organizations. They support data sovereignty, regulatory compliance, and collaborative intelligence without sharing raw data.

Federated Architecture

The federated agent architecture consists of several key components:

1. Federated Orchestrator: Coordinates workflows across organizations
2. Secure Data Exchange: Manages privacy-preserving data sharing
3. Federated Identity: Handles cross-organization authentication and authorization
4. Policy Enforcement: Ensures compliance with data governance policies
5. Audit System: Records all cross-organizational activities

Note: This is a placeholder for a federated agent architecture diagram. The actual diagram should be created and added to the project.

Federated Agent Characteristics

• Privacy-Preserving: Data remains within organizational boundaries.
• Secure Data Sharing: Encrypted, auditable, and purpose-limited sharing.
• Distributed Execution: Workflow steps run in the appropriate org context.
• Federated Learning: Support for distributed model training and aggregation.
• Zero-Knowledge Proofs: Verification without revealing sensitive data.
• Audit Trails: Comprehensive logging of cross-org operations.

Cross-Organizational Data Flow

Federated workflows carefully manage data as it flows between organizations:

Note: This is a placeholder for a cross-organizational data flow diagram. The actual diagram should be created and added to the project.

Development Patterns

Explicit Interfaces

Define clear boundaries and data contracts between organizations:

# Example of explicit interface definition
from meta_agent_platform import FederatedAgent, DataContract

class FinancialRiskAnalysisAgent(FederatedAgent):
    def __init__(self, config):
        super().__init__(config)

        # Define explicit data contract for what this agent needs
        self.input_contract = DataContract(
            fields=[
                {
                    "name": "transaction_amount",
                    "type": "float",
                    "description": "Transaction amount in USD",
                    "required": True,
                    "sensitivity": "medium"
                },
                {
                    "name": "transaction_date",
                    "type": "date",
                    "description": "Date of transaction",
                    "required": True,
                    "sensitivity": "low"
                },
                {
                    "name": "merchant_category",
                    "type": "string",
                    "description": "Category of merchant",
                    "required": True,
                    "sensitivity": "low"
                }
                # Note: PII like customer name is NOT requested
            ],
            purpose="Risk analysis for fraud detection",
            retention_period="30 days"
        )

        # Define what this agent returns
        self.output_contract = DataContract(
            fields=[
                {
                    "name": "risk_score",
                    "type": "float",
                    "description": "Risk score between 0-1",
                    "sensitivity": "medium"
                },
                {
                    "name": "risk_factors",
                    "type": "array",
                    "description": "Factors contributing to risk score",
                    "sensitivity": "medium"
                }
            ],
            purpose="Provide risk assessment",
            retention_period="90 days"
        )

Secure Multi-Party Computation

Perform computations on encrypted data without revealing the inputs:

# Example of secure multi-party computation
from meta_agent_platform import SMPCAgent
import numpy as np

class SecureAverageCalculator(SMPCAgent):
    def __init__(self, config):
        super().__init__(config)
        self.smpc_protocol = config.get('protocol', 'shamir_secret_sharing')

    def process(self, encrypted_inputs):
        # Each organization has provided encrypted inputs
        # We can compute on them without decrypting

        # Initialize SMPC computation context
        with self.smpc_context(protocol=self.smpc_protocol) as ctx:
            # Load the encrypted inputs from each organization
            values = [ctx.load(inp) for inp in encrypted_inputs]

            # Compute average without revealing individual values
            sum_value = ctx.sum(values)
            count = ctx.constant(len(values))
            average = ctx.divide(sum_value, count)

            # Return encrypted result that can only be decrypted
            # with appropriate access rights
            return ctx.encrypt_result(average)

Federated Learning

Train models across organizations without sharing raw data:

# Example of federated learning agent
from meta_agent_platform import FederatedLearningAgent
import numpy as np

class FederatedClassifier(FederatedLearningAgent):
    def __init__(self, config):
        super().__init__(config)
        self.model = self.initialize_model(config.get('model_config'))
        self.aggregation_strategy = config.get('aggregation', 'fedavg')

    def train_round(self, round_num):
        # 1. Send current global model to all participating organizations
        org_models = self.distribute_model(self.model, self.participating_orgs)

        # 2. Each org trains on their local data (happens remotely)
        updated_models = self.collect_trained_models(round_num)

        # 3. Securely aggregate the model updates
        if self.aggregation_strategy == 'fedavg':
            # Simple weighted average of model parameters
            self.model = self.federated_average(updated_models)
        elif self.aggregation_strategy == 'secure_aggregation':
            # Secure aggregation without revealing individual updates
            self.model = self.secure_aggregate(updated_models)

        # 4. Evaluate global model performance
        metrics = self.evaluate_global_model()

        return {
            'round': round_num,
            'metrics': metrics,
            'participating_orgs': len(self.participating_orgs)
        }

Example: Federated Workflow

A complete federated workflow involving multiple organizations:

# federated-workflow.yaml
name: cross-org-loan-approval
version: 1.0.0
type: federated

participants:
  - id: bank-org
    role: workflow-initiator
    permissions: [initiate, read-results]
  - id: credit-bureau-org
    role: data-provider
    permissions: [provide-credit-data]
  - id: risk-assessment-org
    role: service-provider
    permissions: [run-risk-models]

workflow:
  steps:
    - id: loan-application
      organization: bank-org
      agent: loan-application-processor
      next: credit-check

    - id: credit-check
      organization: credit-bureau-org
      agent: credit-data-provider
      data_minimization: true  # Only provide necessary data
      next: risk-assessment

    - id: risk-assessment
      organization: risk-assessment-org
      agent: loan-risk-analyzer
      secure_compute: true  # Use secure computation
      next: decision

    - id: decision
      organization: bank-org
      agent: loan-decision-maker
      next: null

governance:
  data_sharing:
    - from: bank-org
      to: credit-bureau-org
      data_types: [customer_id, inquiry_purpose]
      restrictions: [no-storage, audit-required]

    - from: credit-bureau-org
      to: risk-assessment-org
      data_types: [credit_score, payment_history]
      restrictions: [no-storage, no-forwarding, encrypted-only]

    - from: risk-assessment-org
      to: bank-org
      data_types: [risk_score, risk_factors]
      restrictions: [audit-required]

audit:
  level: comprehensive
  retention: 7-years
  encryption: true

Security Considerations

Data Minimization

Only share the minimum data necessary for the task:

# Example of data minimization
from meta_agent_platform import DataMinimizer

def minimize_customer_data(customer_data, purpose):
    minimizer = DataMinimizer(purpose=purpose)

    if purpose == 'credit_check':
        # Only share fields needed for credit check
        return minimizer.extract_fields(customer_data, [
            'customer_id',
            'income',
            'employment_status',
            # Explicitly NOT sharing: name, address, SSN, etc.
        ])
    elif purpose == 'risk_assessment':
        # Only share fields needed for risk assessment
        return minimizer.extract_fields(customer_data, [
            'loan_amount',
            'loan_purpose',
            'credit_score',
            # Explicitly NOT sharing: customer_id, etc.
        ])
    else:
        raise ValueError(f"Unknown purpose: {purpose}")

Differential Privacy

Add noise to protect individual privacy while maintaining statistical utility:

# Example of differential privacy
from meta_agent_platform import DifferentialPrivacy

def get_private_statistics(sensitive_data, epsilon=0.1):
    # Initialize differential privacy mechanism
    dp = DifferentialPrivacy(epsilon=epsilon)

    # Compute statistics with privacy guarantees
    results = {
        'count': dp.count(sensitive_data),
        'sum': dp.sum(sensitive_data['value']),
        'mean': dp.mean(sensitive_data['value']),
        'histogram': dp.histogram(sensitive_data['category'], bins=10)
    }

    # Include privacy budget information
    results['privacy_guarantee'] = {
        'epsilon': epsilon,
        'delta': 0,
        'mechanism': 'laplace'
    }

    return results

Compliance and Governance

Regulatory Compliance

Implement controls for various regulations:

# Example of regulatory compliance checks
from meta_agent_platform import ComplianceChecker

def verify_compliance(workflow, data_contracts):
    checker = ComplianceChecker()

    # Check GDPR compliance
    gdpr_issues = checker.check_gdpr(workflow, data_contracts)

    # Check HIPAA compliance if health data is involved
    hipaa_issues = []
    if checker.contains_health_data(data_contracts):
        hipaa_issues = checker.check_hipaa(workflow, data_contracts)

    # Check CCPA compliance for California residents
    ccpa_issues = checker.check_ccpa(workflow, data_contracts)

    # Combine all issues
    all_issues = gdpr_issues + hipaa_issues + ccpa_issues

    if all_issues:
        return {
            'compliant': False,
            'issues': all_issues,
            'recommendations': checker.get_recommendations(all_issues)
        }
    else:
        return {
            'compliant': True,
            'certifications': checker.get_certifications(workflow, data_contracts)
        }

Best Practices

• End-to-End Encryption: Encrypt all data in transit and at rest.
• Access Controls: Implement fine-grained permissions for all shared resources.
• Audit Logging: Maintain logs for all federated operations.
• Testing: Simulate multi-org scenarios and validate privacy guarantees.
• Compliance: Document and enforce data handling policies.
• Data Minimization: Share only what's necessary for the specific task.
• Purpose Limitation: Clearly define and enforce data usage purposes.
• Transparency: Provide clear documentation of all cross-org data flows.
• Revocation: Support revoking access to shared data.
• Secure Deletion: Implement verifiable deletion of shared data after use.

Troubleshooting

Issue	Possible Cause	Solution
Authentication failure	Expired credentials	Renew organization credentials, check federation trust
Data access denied	Insufficient permissions	Review data contracts and governance policies
Privacy budget exceeded	Too many queries on sensitive data	Implement query throttling, reset privacy budget
Compliance violation	Missing data protection controls	Add required controls, update data contracts
Performance degradation	Encryption overhead	Optimize cryptographic operations, use hardware acceleration
Trust verification failure	Certificate issues	Update certificates, check trust chain

References

• Federated Collaboration Guide
• Component Design: Federated Collaboration Framework
• Data Model: Federated Entities
• Federated Infrastructure
• OpenMined PySyft
• TensorFlow Federated
• Differential Privacy
• Secure Multi-Party Computation

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Last updated: 2025-04-18