Federated Collaboration Guide
This guide provides best practices, patterns, and resources for implementing secure cross-organization workflows with the AI Agent Orchestration Platform.
1. Introduction to Federated Collaboration
Federated collaboration enables secure workflows across organizational boundaries while maintaining data privacy and sovereignty. Key benefits include:
- Privacy Preservation: Keep sensitive data within organizational boundaries
- Regulatory Compliance: Meet data residency and sovereignty requirements
- Collective Intelligence: Leverage insights across organizations without raw data sharing
- Resource Sharing: Access specialized agents and capabilities from partners
- Distributed Governance: Maintain control over organizational assets and policies
2. Federated Architecture
2.1 Federated Collaboration Model
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Organization A │◄────►│ Secure Gateway │◄────►│ Organization B │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Local Agents │ │ Access Control │ │ Local Agents │
│ Private Data │ │ Audit Logging │ │ Private Data │
│ Local Workflows │ │ Secure Compute │ │ Local Workflows │
└─────────────────┘ └─────────────────┘ └─────────────────┘
2.2 Federated Components
federated/
├── gateway/
│ ├── secure_gateway.py
│ ├── access_control.py
│ └── audit_logger.py
├── collaboration/
│ ├── federated_workflow.py
│ ├── secure_compute.py
│ └── data_sharing.py
├── learning/
│ ├── federated_learning.py
│ ├── secure_aggregation.py
│ └── differential_privacy.py
├── crypto/
│ ├── homomorphic.py
│ ├── zero_knowledge.py
│ └── secure_multiparty.py
└── registry/
├── federated_registry.py
└── capability_discovery.py
3. Secure Data Sharing
3.1 Data Sharing Principles
- Minimal Disclosure: Share only what's necessary
- Purpose Limitation: Define clear purposes for shared data
- Access Controls: Implement fine-grained permissions
- Audit Trails: Log all access and operations
- Encryption: Protect data in transit and at rest
- Time Limitations: Set expiration for shared access
3.2 Secure Data Sharing Implementation
import json
import uuid
import time
import hashlib
import base64
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import padding, rsa
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
class SecureDataSharing:
"""Manage secure data sharing between organizations."""
def __init__(self, org_id, private_key=None, public_keys=None):
self.org_id = org_id
self.private_key = private_key
self.public_keys = public_keys or {}
self.access_log = []
def share_data(self, data, recipient_org_id, purpose, expiration_seconds=3600):
"""Share data securely with another organization."""
if recipient_org_id not in self.public_keys:
raise ValueError(f"Public key for organization {recipient_org_id} not found")
# Generate a unique sharing ID
sharing_id = str(uuid.uuid4())
# Generate a symmetric key for this sharing
symmetric_key = Fernet.generate_key()
# Encrypt the data with the symmetric key
f = Fernet(symmetric_key)
encrypted_data = f.encrypt(json.dumps(data).encode())
# Encrypt the symmetric key with the recipient's public key
recipient_public_key = self.public_keys[recipient_org_id]
encrypted_key = recipient_public_key.encrypt(
symmetric_key,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
# Create sharing metadata
metadata = {
"sharing_id": sharing_id,
"source_org_id": self.org_id,
"recipient_org_id": recipient_org_id,
"purpose": purpose,
"created_at": time.time(),
"expires_at": time.time() + expiration_seconds,
"data_hash": hashlib.sha256(json.dumps(data).encode()).hexdigest()
}
# Log the sharing
self._log_access("share", sharing_id, recipient_org_id, purpose)
return {
"sharing_id": sharing_id,
"encrypted_data": base64.b64encode(encrypted_data).decode(),
"encrypted_key": base64.b64encode(encrypted_key).decode(),
"metadata": metadata
}
def receive_data(self, encrypted_package):
"""Receive and decrypt shared data."""
if not self.private_key:
raise ValueError("Private key not available for decryption")
# Extract components from the package
sharing_id = encrypted_package["sharing_id"]
encrypted_data = base64.b64decode(encrypted_package["encrypted_data"])
encrypted_key = base64.b64decode(encrypted_package["encrypted_key"])
metadata = encrypted_package["metadata"]
# Verify recipient
if metadata["recipient_org_id"] != self.org_id:
raise ValueError("This data is not intended for this organization")
# Check expiration
if metadata["expires_at"] < time.time():
raise ValueError("This shared data has expired")
# Decrypt the symmetric key
symmetric_key = self.private_key.decrypt(
encrypted_key,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
# Decrypt the data
f = Fernet(symmetric_key)
decrypted_data = json.loads(f.decrypt(encrypted_data).decode())
# Verify data integrity
data_hash = hashlib.sha256(json.dumps(decrypted_data).encode()).hexdigest()
if data_hash != metadata["data_hash"]:
raise ValueError("Data integrity check failed")
# Log the access
self._log_access("receive", sharing_id, metadata["source_org_id"], metadata["purpose"])
return {
"data": decrypted_data,
"metadata": metadata
}
def _log_access(self, action, sharing_id, org_id, purpose):
"""Log data sharing access."""
self.access_log.append({
"timestamp": time.time(),
"action": action,
"sharing_id": sharing_id,
"org_id": org_id,
"purpose": purpose
})
# In a real implementation, this would be persisted to a secure audit log
print(f"[LOG] {action.upper()} - Sharing ID: {sharing_id}, Org: {org_id}, Purpose: {purpose}")
4. Federated Workflows
4.1 Federated Workflow Principles
- Clear Boundaries: Define explicit interfaces between organizations
- Minimal Data Transfer: Only share necessary data between workflow steps
- Distributed Execution: Run steps in the appropriate organizational context
- Coordinated Orchestration: Maintain workflow state across boundaries
- Failure Handling: Implement robust error handling and recovery
- Audit Trail: Maintain comprehensive logs of cross-organization execution
4.2 Federated Workflow Implementation
import uuid
import time
import json
from enum import Enum
from typing import Dict, List, Any, Optional
class StepStatus(Enum):
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
WAITING_FOR_APPROVAL = "waiting_for_approval"
class FederatedWorkflow:
"""Manage workflows that span multiple organizations."""
def __init__(self, workflow_id=None, name=None, description=None):
self.workflow_id = workflow_id or str(uuid.uuid4())
self.name = name or f"Federated Workflow {self.workflow_id[:8]}"
self.description = description
self.steps = []
self.connections = []
self.current_state = {}
self.execution_log = []
def add_step(self, step_id, org_id, agent_id, config=None, approval_required=False):
"""Add a step to the workflow."""
step = {
"step_id": step_id,
"org_id": org_id,
"agent_id": agent_id,
"config": config or {},
"approval_required": approval_required,
"status": StepStatus.PENDING.value,
"result": None,
"error": None,
"start_time": None,
"end_time": None
}
self.steps.append(step)
return self
def add_connection(self, from_step_id, to_step_id, data_mapping=None):
"""Add a connection between steps."""
connection = {
"from_step_id": from_step_id,
"to_step_id": to_step_id,
"data_mapping": data_mapping or {}
}
self.connections.append(connection)
return self
def get_next_steps(self, completed_step_id=None):
"""Get the next steps that can be executed."""
if completed_step_id is None:
# Find steps with no incoming connections (starting points)
incoming_steps = set(conn["to_step_id"] for conn in self.connections)
return [step for step in self.steps if step["step_id"] not in incoming_steps]
# Find steps connected to the completed step
next_step_ids = [conn["to_step_id"] for conn in self.connections if conn["from_step_id"] == completed_step_id]
next_steps = [step for step in self.steps if step["step_id"] in next_step_ids]
# Check if all incoming connections to these steps are from completed steps
ready_steps = []
for step in next_steps:
incoming_connections = [conn for conn in self.connections if conn["to_step_id"] == step["step_id"]]
all_dependencies_met = True
for conn in incoming_connections:
from_step = next((s for s in self.steps if s["step_id"] == conn["from_step_id"]), None)
if not from_step or from_step["status"] != StepStatus.COMPLETED.value:
all_dependencies_met = False
break
if all_dependencies_met:
ready_steps.append(step)
return ready_steps
def prepare_step_input(self, step_id):
"""Prepare input data for a step based on connections."""
step = next((s for s in self.steps if s["step_id"] == step_id), None)
if not step:
raise ValueError(f"Step {step_id} not found")
# Find all incoming connections to this step
incoming_connections = [conn for conn in self.connections if conn["to_step_id"] == step_id]
# Prepare input data from connected steps
input_data = {}
for conn in incoming_connections:
from_step = next((s for s in self.steps if s["step_id"] == conn["from_step_id"]), None)
if not from_step or from_step["status"] != StepStatus.COMPLETED.value or from_step["result"] is None:
continue
# Apply data mapping
for source_key, target_key in conn["data_mapping"].items():
if source_key in from_step["result"]:
input_data[target_key] = from_step["result"][source_key]
# Merge with step config
return {**step["config"], "input": input_data}
def execute_step(self, step_id, agent_executor):
"""Execute a single step of the workflow."""
step = next((s for s in self.steps if s["step_id"] == step_id), None)
if not step:
raise ValueError(f"Step {step_id} not found")
# Check if step is ready to execute
if step["status"] not in [StepStatus.PENDING.value, StepStatus.WAITING_FOR_APPROVAL.value]:
return False
# If approval is required, check if it's been approved
if step["approval_required"] and step["status"] != StepStatus.WAITING_FOR_APPROVAL.value:
step["status"] = StepStatus.WAITING_FOR_APPROVAL.value
self._log_execution(step_id, "waiting_for_approval", None)
return False
# Prepare input data
input_data = self.prepare_step_input(step_id)
# Update step status
step["status"] = StepStatus.RUNNING.value
step["start_time"] = time.time()
self._log_execution(step_id, "started", input_data)
try:
# Execute the agent
result = agent_executor(step["org_id"], step["agent_id"], input_data)
# Update step with result
step["status"] = StepStatus.COMPLETED.value
step["result"] = result
step["end_time"] = time.time()
self._log_execution(step_id, "completed", result)
return True
except Exception as e:
# Handle execution error
step["status"] = StepStatus.FAILED.value
step["error"] = str(e)
step["end_time"] = time.time()
self._log_execution(step_id, "failed", {"error": str(e)})
return False
def approve_step(self, step_id, approver_id, comments=None):
"""Approve a step that requires approval."""
step = next((s for s in self.steps if s["step_id"] == step_id), None)
if not step:
raise ValueError(f"Step {step_id} not found")
if step["status"] != StepStatus.WAITING_FOR_APPROVAL.value:
raise ValueError(f"Step {step_id} is not waiting for approval")
# Update step status
step["status"] = StepStatus.PENDING.value
self._log_execution(step_id, "approved", {
"approver_id": approver_id,
"comments": comments
})
return True
def reject_step(self, step_id, approver_id, reason=None):
"""Reject a step that requires approval."""
step = next((s for s in self.steps if s["step_id"] == step_id), None)
if not step:
raise ValueError(f"Step {step_id} not found")
if step["status"] != StepStatus.WAITING_FOR_APPROVAL.value:
raise ValueError(f"Step {step_id} is not waiting for approval")
# Update step status
step["status"] = StepStatus.FAILED.value
step["error"] = f"Rejected by {approver_id}: {reason or 'No reason provided'}"
self._log_execution(step_id, "rejected", {
"approver_id": approver_id,
"reason": reason
})
return True
def _log_execution(self, step_id, action, data):
"""Log workflow execution events."""
log_entry = {
"timestamp": time.time(),
"workflow_id": self.workflow_id,
"step_id": step_id,
"action": action,
"data": data
}
self.execution_log.append(log_entry)
# In a real implementation, this would be persisted to a secure audit log
print(f"[WORKFLOW] {action.upper()} - Workflow: {self.workflow_id}, Step: {step_id}")
5. Federated Learning
5.1 Federated Learning Principles
- Local Training: Train models on local data within organizational boundaries
- Secure Aggregation: Combine model updates without revealing raw data
- Differential Privacy: Add noise to protect individual data points
- Model Encryption: Encrypt model parameters during transmission
- Secure Enclaves: Use trusted execution environments for sensitive operations
- Decentralized Coordination: Distribute coordination to avoid central points of failure
5.2 Federated Learning Implementation
import numpy as np
import time
import uuid
import json
from typing import List, Dict, Any, Optional
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
import os
class FederatedLearning:
"""Implement federated learning across organizations."""
def __init__(self, task_id=None, model_type=None, aggregation_method="fedavg"):
self.task_id = task_id or str(uuid.uuid4())
self.model_type = model_type
self.aggregation_method = aggregation_method
self.participants = []
self.global_model = None
self.round = 0
self.training_log = []
def add_participant(self, org_id, data_samples=0, compute_capacity=1.0):
"""Add a participating organization."""
participant = {
"org_id": org_id,
"data_samples": data_samples, # Number of training samples (for weighting)
"compute_capacity": compute_capacity, # Relative compute capacity
"status": "registered",
"last_update": None,
"local_accuracy": None,
"rounds_participated": 0
}
self.participants.append(participant)
self._log_event("participant_added", {"org_id": org_id})
return self
def initialize_global_model(self, model_params):
"""Initialize the global model parameters."""
self.global_model = {
"params": model_params,
"metadata": {
"created_at": time.time(),
"updated_at": time.time(),
"version": 1
}
}
self._log_event("global_model_initialized", {"version": 1})
return self.global_model
def get_global_model(self):
"""Get the current global model."""
if not self.global_model:
raise ValueError("Global model has not been initialized")
return self.global_model
def submit_model_update(self, org_id, model_update, metrics=None, encryption_key=None):
"""Submit a local model update from a participant."""
participant = next((p for p in self.participants if p["org_id"] == org_id), None)
if not participant:
raise ValueError(f"Organization {org_id} is not a registered participant")
# Decrypt model update if encrypted
if encryption_key:
model_update = self._decrypt_model_update(model_update, encryption_key)
# Update participant status
participant["status"] = "update_submitted"
participant["last_update"] = time.time()
participant["local_accuracy"] = metrics.get("accuracy") if metrics else None
participant["rounds_participated"] += 1
# Store the update (in a real implementation, this would be in a secure database)
update_id = str(uuid.uuid4())
update_record = {
"update_id": update_id,
"org_id": org_id,
"round": self.round,
"model_update": model_update,
"metrics": metrics,
"timestamp": time.time()
}
self._log_event("model_update_received", {
"org_id": org_id,
"round": self.round,
"update_id": update_id
})
return update_id
def aggregate_updates(self, update_ids=None, differential_privacy=False, dp_epsilon=1.0):
"""Aggregate model updates to create a new global model."""
if not self.global_model:
raise ValueError("Global model has not been initialized")
# In a real implementation, we would retrieve updates from a secure database
# Here we assume updates are available in memory
updates = [] # This would be populated with actual updates
# Get participant weights based on data samples
total_samples = sum(p["data_samples"] for p in self.participants)
weights = {p["org_id"]: p["data_samples"] / total_samples if total_samples > 0 else 1.0 / len(self.participants)
for p in self.participants}
# Perform weighted aggregation (FedAvg algorithm)
aggregated_params = self._fedavg(updates, weights)
# Apply differential privacy if requested
if differential_privacy:
aggregated_params = self._apply_differential_privacy(aggregated_params, dp_epsilon)
# Update the global model
self.round += 1
self.global_model["params"] = aggregated_params
self.global_model["metadata"]["updated_at"] = time.time()
self.global_model["metadata"]["version"] += 1
self._log_event("global_model_updated", {
"round": self.round,
"version": self.global_model["metadata"]["version"],
"participants": len(updates)
})
# Reset participant status for next round
for participant in self.participants:
participant["status"] = "registered"
return self.global_model
def _fedavg(self, updates, weights):
"""Implement Federated Averaging algorithm."""
# This is a simplified implementation
# In a real system, this would handle tensors and model architectures
if not updates:
return self.global_model["params"]
aggregated_params = {}
for param_name in self.global_model["params"]:
# Initialize with zeros of the same shape
param_shape = np.array(self.global_model["params"][param_name]).shape
aggregated_params[param_name] = np.zeros(param_shape)
# Weighted sum of updates
for update, org_id in updates:
if param_name in update:
weight = weights.get(org_id, 1.0 / len(updates))
aggregated_params[param_name] += weight * np.array(update[param_name])
return aggregated_params
def _apply_differential_privacy(self, params, epsilon):
"""Apply differential privacy to model parameters."""
# This is a simplified implementation
# In a real system, this would use proper DP mechanisms like Gaussian or Laplace
noised_params = {}
for param_name, param_value in params.items():
param_array = np.array(param_value)
sensitivity = 1.0 # This would be calculated based on clipping bounds
noise_scale = sensitivity / epsilon
noise = np.random.laplace(0, noise_scale, param_array.shape)
noised_params[param_name] = param_array + noise
return noised_params
def _encrypt_model_update(self, model_update, key):
"""Encrypt model update for secure transmission."""
# This is a simplified implementation
# In a real system, this would use proper encryption libraries
# Convert model update to JSON string
update_json = json.dumps(model_update)
# Generate a random IV
iv = os.urandom(16)
# Create an AES cipher
cipher = Cipher(algorithms.AES(key), modes.CFB(iv))
encryptor = cipher.encryptor()
# Encrypt the update
ciphertext = encryptor.update(update_json.encode()) + encryptor.finalize()
return {
"iv": iv.hex(),
"ciphertext": ciphertext.hex()
}
def _decrypt_model_update(self, encrypted_update, key):
"""Decrypt an encrypted model update."""
# This is a simplified implementation
# Extract IV and ciphertext
iv = bytes.fromhex(encrypted_update["iv"])
ciphertext = bytes.fromhex(encrypted_update["ciphertext"])
# Create an AES cipher
cipher = Cipher(algorithms.AES(key), modes.CFB(iv))
decryptor = cipher.decryptor()
# Decrypt the update
plaintext = decryptor.update(ciphertext) + decryptor.finalize()
# Parse the JSON
return json.loads(plaintext.decode())
def _log_event(self, event_type, data):
"""Log federated learning events."""
log_entry = {
"timestamp": time.time(),
"task_id": self.task_id,
"event_type": event_type,
"round": self.round,
"data": data
}
self.training_log.append(log_entry)
# In a real implementation, this would be persisted to a secure audit log
print(f"[FEDERATED] {event_type.upper()} - Task: {self.task_id}, Round: {self.round}")
6. Zero-Knowledge Proofs
6.1 Zero-Knowledge Proof Principles
- Verification Without Disclosure: Prove a statement is true without revealing the underlying data
- Completeness: If the statement is true, an honest verifier will be convinced
- Soundness: If the statement is false, no cheating prover can convince the verifier
- Zero-Knowledge: The verifier learns nothing beyond the validity of the statement
- Non-Interactive: Proofs can be verified without interaction with the prover
- Succinctness: Proofs are compact and quick to verify
6.2 Use Cases in Federated Collaboration
- Compliance Verification: Prove regulatory compliance without revealing sensitive data
- Model Validation: Verify model properties without exposing the model itself
- Data Quality Assurance: Prove data meets quality standards without sharing raw data
- Identity Verification: Authenticate organizations without revealing credentials
- Audit Trail Verification: Prove audit trail integrity without exposing details
7. Security Considerations
7.1 Threat Model
- Honest-but-Curious Participants: Organizations follow the protocol but may try to learn others' data
- Malicious Participants: Organizations may deviate from the protocol to compromise security
- External Attackers: Third parties may attempt to compromise the federated system
- Insider Threats: Employees with access to organizational systems may misuse data
- Infrastructure Vulnerabilities: Weaknesses in the underlying infrastructure
7.2 Security Best Practices
- End-to-End Encryption: Encrypt all data in transit and at rest
- Access Controls: Implement fine-grained permissions for all resources
- Audit Logging: Maintain comprehensive logs of all operations
- Secure Key Management: Use hardware security modules for key storage
- Regular Security Audits: Conduct penetration testing and code reviews
- Secure Multi-Party Computation: Use SMC for sensitive operations
- Differential Privacy: Apply DP techniques to protect individual data points
- Secure Enclaves: Use trusted execution environments for sensitive computations
8. Testing and Validation
8.1 Testing Strategies
- Unit Testing: Test individual components in isolation
- Integration Testing: Test interactions between components
- End-to-End Testing: Test complete federated workflows
- Security Testing: Verify security measures are effective
- Performance Testing: Measure latency and throughput
- Compliance Testing: Verify regulatory requirements are met
8.2 Validation Framework
- Correctness Validation: Verify results match expected outcomes
- Security Validation: Verify security measures are effective
- Privacy Validation: Verify privacy guarantees are maintained
- Performance Validation: Verify performance meets requirements
- Compliance Validation: Verify regulatory requirements are met
9. Resources and References
- Federated Learning: TensorFlow Federated, PySyft
- Secure Multi-Party Computation: MP-SPDZ, SCALE-MAMBA
- Differential Privacy: OpenDP, Google Differential Privacy
- Zero-Knowledge Proofs: ZoKrates, Circom
- Secure Enclaves: Intel SGX, AMD SEV
This guide will evolve as the platform's federated collaboration capabilities expand. Contribute your insights and improvements to help build a robust federated collaboration ecosystem.