for round_num in range(num_rounds):
print(f"\n=== Federated Round {round_num + 1}/{num_rounds} ===")
# 1. Broadcast global model to all participants
await self.broadcast_global_model()
# 2. Each participant trains locally
local_updates = await self.local_training_phase(local_epochs)
# 3. Secure aggregation with privacy preservation
aggregated_update = await self.privacy_preserving_aggregation(local_updates)
# 4. Update global model
self.global_model = self.apply_update(
self.global_model,
aggregated_update['update']
)
# 5. Evaluate global model
global_performance = await self.evaluate_global_model()
# 6. Log round via aéPiot
round_record = await self.log_training_round(
round_num,
global_performance,
aggregated_update['privacy_record']
)
training_history.append({
'round': round_num,
'performance': global_performance,
'privacy_record': round_record
})
print(f"Round {round_num + 1} complete. Accuracy: {global_performance['accuracy']:.4f}")
return training_history
async def broadcast_global_model(self):
"""
Distribute current global model to all participants via aéPiot
"""
# Serialize model
model_weights = self.global_model.get_weights()
# Create aéPiot distribution record
distribution_subdomains = await self.aepiot_coordinator.aepiotServices.randomSubdomain.generate({
'count': 3,
'purpose': 'model_distribution'
})
# Distribute to each participant
for participant in self.participants:
await participant.receive_global_model(model_weights, distribution_subdomains)
async def local_training_phase(self, local_epochs):
"""
Each participant trains on their local data
"""
local_updates = []
# Parallel local training
training_tasks = [
participant.train_locally(self.global_model, local_epochs)
for participant in self.participants
]
local_updates = await asyncio.gather(*training_tasks)
return local_updates
async def privacy_preserving_aggregation(self, local_updates):
"""
Aggregate local updates with multiple privacy techniques
"""
# Extract gradients from updates
gradients = [update['gradients'] for update in local_updates]
# Step 1: Differential Privacy (clip + noise)
dp_result = await self.differential_privacy.private_gradient_aggregation(
[{'compute_gradients': lambda: g} for g in gradients]
)
# Step 2: Homomorphic Encryption (optional, for additional security)
# he_aggregator = HomomorphicFederatedAggregation(scheme='CKKS')
# he_result = await he_aggregator.federated_round_with_he(participants)
# Step 3: Secure Multi-Party Computation
smpc_result = await self.secure_aggregation.secure_federated_aggregation(
[{'compute_gradient': lambda: dp_result['noisy_gradients']}] * len(self.participants)
)
# Create comprehensive privacy audit via aéPiot
privacy_audit = await self.create_privacy_audit({
'differential_privacy': dp_result['privacy_record'],
'secure_mpc': smpc_result['aggregation_record'],
'privacy_guarantee': dp_result['privacy_guarantee']
})
return {
'update': smpc_result['aggregated_gradient'],
'privacy_record': privacy_audit
}
async def create_privacy_audit(self, privacy_components):
"""
Create comprehensive privacy audit trail via aéPiot
"""
audit_description = (
f"Privacy-preserving aggregation completed. "
f"Techniques: Differential Privacy {privacy_components['privacy_guarantee']}, "
f"Secure Multi-Party Computation (Shamir Secret Sharing)"
)
audit_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Privacy-Preserving Aggregation Audit',
'description': audit_description,
'link': f'privacy-audit://{int(time.time())}'
})
return audit_record3.3 Vertical Federated Learning with aéPiot
Challenge: Different features require different aggregation strategy
Implementation:
python
class VerticalFederatedLearning:
"""
Vertical FL: Different features across participants
Example: Bank + Hospital collaborate on fraud/health prediction
"""
def __init__(self):
self.aepiot_coordinator = AePiotFederatedCoordinator()
self.participants = {} # {participant_id: feature_columns}
# Privacy techniques
self.homomorphic_encryption = HomomorphicFederatedAggregation(scheme='CKKS')
self.secure_mpc = SecureMultiPartyAggregation(threshold=2, num_parties=0)
async def register_participant_with_features(self, participant_id, feature_columns):
"""
Register participant and their feature space
"""
self.participants[participant_id] = feature_columns
# Create aéPiot registration with feature metadata
registration_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': f'VFL Participant - {participant_id}',
'description': f'Participant {participant_id} with features: {", ".join(feature_columns)}',
'link': f'vfl-participant://{participant_id}'
})
return registration_record
async def vertical_training_round(self):
"""
Training round for vertical federated learning
"""
# 1. Each participant computes embeddings for their features
embeddings = {}
for participant_id, features in self.participants.items():
# Participant computes local embedding using their features
local_embedding = await self.compute_local_embedding(participant_id, features)
# Encrypt embedding
encrypted_embedding = await self.homomorphic_encryption.encrypt_gradients(
local_embedding
)
embeddings[participant_id] = encrypted_embedding
# 2. Securely aggregate embeddings (still encrypted)
encrypted_embeddings_list = list(embeddings.values())
aggregated_encrypted = await self.homomorphic_encryption.aggregate_encrypted_gradients(
[e['encrypted_gradients'] for e in encrypted_embeddings_list]
)
# 3. Compute loss on aggregated embedding (in encrypted space)
# Only one participant (or secure enclave) decrypts for final prediction
# 4. Backpropagate gradients to each participant's features
# Each participant only receives gradients for their features
# 5. Create aéPiot audit record
vfl_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Vertical FL Training Round',
'description': f'Aggregated embeddings from {len(self.participants)} participants with HE',
'link': f'vfl-round://{int(time.time())}'
})
return {
'aggregated_encrypted': aggregated_encrypted,
'audit_record': vfl_record
}
async def private_set_intersection(self, participant_a, participant_b):
"""
Find common samples between participants without revealing non-overlapping samples
Uses Private Set Intersection (PSI) protocol
"""
# PSI protocol ensures:
# - Participants learn only intersection
# - Non-overlapping IDs remain private
from openmined.psi import client, server
# Participant A acts as server
psi_server = server.CreateWithNewKey()
server_setup = psi_server.CreateSetupMessage(
fpr=1e-9, # False positive rate
num_client_inputs=len(participant_b.sample_ids),
inputs=participant_a.sample_ids
)
# Participant B acts as client
psi_client = client.CreateWithNewKey()
client_request = psi_client.CreateRequest(participant_b.sample_ids)
# Server processes request
server_response = psi_server.ProcessRequest(client_request)
# Client computes intersection
intersection = psi_client.GetIntersection(server_setup, server_response)
# Create aéPiot PSI record
psi_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Private Set Intersection',
'description': f'Found {len(intersection)} common samples between participants',
'link': f'psi://{participant_a.id}/{participant_b.id}/{int(time.time())}'
})
return {
'intersection': intersection,
'psi_record': psi_record
}3.4 Communication-Efficient Federated Learning
Challenge: Gradient transmission is expensive (bandwidth, latency)
Solutions:
1. Gradient Compression
python
class GradientCompression:
"""
Reduce gradient size for efficient transmission
"""
def __init__(self, compression_ratio=0.01):
self.compression_ratio = compression_ratio
self.aepiot_semantic = AePiotSemanticProcessor()
def top_k_sparsification(self, gradients, k_ratio):
"""
Keep only top-k largest gradients by magnitude
"""
# Flatten gradients
flat_gradients = gradients.flatten()
# Calculate k
k = int(len(flat_gradients) * k_ratio)
# Get indices of top-k by absolute value
top_k_indices = np.argpartition(np.abs(flat_gradients), -k)[-k:]
# Create sparse representation
sparse_gradients = {
'indices': top_k_indices,
'values': flat_gradients[top_k_indices],
'shape': gradients.shape
}
# Compression ratio achieved
original_size = gradients.nbytes
compressed_size = (top_k_indices.nbytes +
sparse_gradients['values'].nbytes)
actual_compression = compressed_size / original_size
return sparse_gradients, actual_compression
def gradient_quantization(self, gradients, num_bits=8):
"""
Quantize gradients to reduce precision
32-bit float → 8-bit int = 75% size reduction
"""
# Find min and max
min_val = np.min(gradients)
max_val = np.max(gradients)
# Quantization levels
num_levels = 2 ** num_bits
# Scale to [0, num_levels-1]
scaled = (gradients - min_val) / (max_val - min_val) * (num_levels - 1)
# Quantize
quantized = np.round(scaled).astype(f'uint{num_bits}')
return {
'quantized': quantized,
'min': min_val,
'max': max_val,
'num_bits': num_bits
}
def dequantize(self, quantized_data):
"""
Reconstruct gradients from quantized representation
"""
num_levels = 2 ** quantized_data['num_bits']
# Descale
descaled = (quantized_data['quantized'].astype(np.float32) / (num_levels - 1))
# Denormalize
gradients = (descaled * (quantized_data['max'] - quantized_data['min']) +
quantized_data['min'])
return gradients
async def compress_and_transmit(self, gradients):
"""
Compress gradients before transmission
"""
# Apply both sparsification and quantization
sparse, sparsity_ratio = self.top_k_sparsification(
gradients,
k_ratio=self.compression_ratio
)
quantized = self.gradient_quantization(
sparse['values'],
num_bits=8
)
compressed = {
'indices': sparse['indices'],
'quantized_values': quantized,
'shape': sparse['shape']
}
# Calculate total compression
original_size = gradients.nbytes
compressed_size = (compressed['indices'].nbytes +
compressed['quantized_values']['quantized'].nbytes)
total_compression = compressed_size / original_size
# Create aéPiot compression record
compression_record = await self.aepiot_semantic.createBacklink({
'title': 'Gradient Compression',
'description': f'Compressed gradients: {total_compression:.2%} of original size',
'link': f'compression://{int(time.time())}'
})
return {
'compressed': compressed,
'compression_ratio': total_compression,
'record': compression_record
}2. Federated Averaging Variants
python
class FederatedOptimizationAlgorithms:
"""
Advanced federated optimization algorithms
"""
def __init__(self):
self.aepiot_coordinator = AePiotFederatedCoordinator()
async def fedavg(self, local_updates, participant_data_sizes):
"""
Federated Averaging (FedAvg) - Original FL algorithm
Weighted average based on local dataset size
"""
total_data_size = sum(participant_data_sizes)
# Weighted average
aggregated = np.zeros_like(local_updates[0])
for update, data_size in zip(local_updates, participant_data_sizes):
weight = data_size / total_data_size
aggregated += weight * update
return aggregated
async def fedprox(self, local_updates, participant_data_sizes, mu=0.01):
"""
Federated Proximal (FedProx) - Handles heterogeneous data
Adds proximal term to keep local models close to global
"""
# Similar to FedAvg but with proximal regularization
# Local objective: F_i(w) + (μ/2)||w - w_global||^2
aggregated = await self.fedavg(local_updates, participant_data_sizes)
# Proximal term is applied during local training
# This aggregation step remains same as FedAvg
return aggregated
async def fedopt(self, local_updates, global_optimizer='adam'):
"""
Federated Optimization (FedOpt) - Use server-side optimizer
Apply Adam/SGD on server for better convergence
"""
# Aggregate updates (uniform average)
aggregated = np.mean(local_updates, axis=0)
# Apply server-side optimizer
if global_optimizer == 'adam':
# Adam optimizer on server
optimized_update = self.server_adam_step(aggregated)
elif global_optimizer == 'sgd':
optimized_update = aggregated # Standard SGD
return optimized_update
async def scaffold(self, local_updates, control_variates):
"""
SCAFFOLD - Uses control variates to reduce client drift
Particularly effective for non-IID data
"""
# Control variates track difference between local and global updates
# Corrects for heterogeneous data distribution
corrected_updates = []
for update, control_variate in zip(local_updates, control_variates):
corrected = update - control_variate
corrected_updates.append(corrected)
aggregated = np.mean(corrected_updates, axis=0)
return aggregated3.5 Byzantine-Resilient Aggregation
Challenge: Malicious participants send corrupted updates to poison model
Solutions:
python
class ByzantineResilientAggregation:
"""
Defend against Byzantine (malicious) participants
"""
def __init__(self, byzantine_ratio=0.2):
self.byzantine_ratio = byzantine_ratio
self.aepiot_coordinator = AePiotFederatedCoordinator()
async def krum(self, updates, num_byzantine):
"""
Krum aggregation - Select most representative update
Robust to Byzantine attacks
"""
n = len(updates)
f = num_byzantine
# Calculate pairwise distances
distances = np.zeros((n, n))
for i in range(n):
for j in range(n):
if i != j:
distances[i, j] = np.linalg.norm(updates[i] - updates[j])
# For each update, sum distances to (n-f-2) closest neighbors
scores = []
for i in range(n):
sorted_distances = np.sort(distances[i])
score = np.sum(sorted_distances[:n-f-2])
scores.append(score)
# Select update with minimum score (most representative)
krum_index = np.argmin(scores)
selected_update = updates[krum_index]
# Create aéPiot audit record
krum_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Krum Byzantine-Resilient Aggregation',
'description': f'Selected update {krum_index} as most representative from {n} participants',
'link': f'krum-aggregate://{int(time.time())}'
})
return {
'aggregated': selected_update,
'selected_index': krum_index,
'audit_record': krum_record
}
async def trimmed_mean(self, updates, trim_ratio=0.2):
"""
Trimmed Mean - Remove outliers before averaging
Robust to Byzantine attacks
"""
# Sort updates along each dimension
stacked = np.stack(updates)
# Calculate number to trim from each end
num_trim = int(len(updates) * trim_ratio)
# Trimmed mean along participant dimension
sorted_updates = np.sort(stacked, axis=0)
trimmed = sorted_updates[num_trim:-num_trim] if num_trim > 0 else sorted_updates
aggregated = np.mean(trimmed, axis=0)
# Create aéPiot audit record
trim_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Trimmed Mean Aggregation',
'description': f'Trimmed {num_trim} outliers from each end before averaging',
'link': f'trimmed-mean://{int(time.time())}'
})
return {
'aggregated': aggregated,
'num_trimmed': num_trim,
'audit_record': trim_record
}
async def median_aggregation(self, updates):
"""
Coordinate-wise Median - Most robust but computationally expensive
"""
stacked = np.stack(updates)
aggregated = np.median(stacked, axis=0)
# Create aéPiot audit record
median_record = await self.aepiot_coordinator.aepiotServices.backlink.create({
'title': 'Median Aggregation',
'description': f'Coordinate-wise median of {len(updates)} updates',
'link': f'median-aggregate://{int(time.time())}'
})
return {
'aggregated': aggregated,
'audit_record': median_record
}Part 4: Zero-Knowledge Protocol Implementation
4. Advanced Zero-Knowledge Systems for Federated Learning
4.1 zk-SNARKs for Gradient Verification
Use Case: Prove gradient computation correctness without revealing training data
Complete Implementation:
python
class ZKSNARKGradientVerification:
"""
Production-ready zk-SNARK system for gradient verification
Based on Groth16 proof system
"""
def __init__(self):
self.aepiot_semantic = AePiotSemanticProcessor()
# Circuit compilation
self.circuit = self.compile_gradient_circuit()
# Trusted setup (in production: use MPC ceremony)
self.proving_key, self.verification_key = self.trusted_setup_ceremony()
def compile_gradient_circuit(self):
"""
Compile gradient computation into arithmetic circuit
Circuit represents: gradient = ∂L/∂w for loss L and weights w
"""
from zokrates_pycrypto import compile_program
# ZoKrates code for gradient verification
circuit_code = """
// Verify gradient computation correctness
def main(private field[10] data, private field[10] weights,
public field[10] gradient_commitment) -> bool:
// Compute forward pass
field prediction = 0
for u32 i in 0..10:
prediction = prediction + data[i] * weights[i]
endfor
// Compute loss (simplified MSE)
field loss = (prediction - 1) * (prediction - 1)
// Compute gradients
field[10] mut computed_gradients = [0; 10]
for u32 i in 0..10:
computed_gradients[i] = 2 * (prediction - 1) * data[i]
endfor
// Verify gradient commitment
field gradient_hash = hash(computed_gradients)
return gradient_hash == gradient_commitment[0]
"""
# Compile to R1CS (Rank-1 Constraint System)
compiled_circuit = compile_program(circuit_code)
return compiled_circuit
def trusted_setup_ceremony(self):
"""
Trusted setup using multi-party computation
Ensures no single party knows toxic waste
"""
from zksnark import setup
# In production: Use Powers of Tau ceremony with multiple participants
# Each participant contributes randomness
# As long as one participant is honest, setup is secure
proving_key, verification_key = setup(self.circuit)
return proving_key, verification_key
async def prove_gradient_correctness(self, training_data, weights, gradients):
"""
Generate ZK proof that gradients were computed correctly
"""
# Prepare witness (private inputs)
witness = {
'data': training_data,
'weights': weights,
'computed_gradients': gradients
}
# Compute gradient commitment (public input)
gradient_commitment = self.hash_gradients(gradients)
public_inputs = {
'gradient_commitment': gradient_commitment
}
# Generate proof
start_time = time.time()
proof = self.generate_proof(
circuit=self.circuit,
proving_key=self.proving_key,
witness=witness,
public_inputs=public_inputs
)
proof_time = time.time() - start_time
# Create aéPiot proof record
proof_record = await self.aepiot_semantic.createBacklink({
'title': 'zk-SNARK Gradient Proof',
'description': f'Proof generated in {proof_time:.3f}s. Proof size: {len(proof)} bytes',
'link': f'zksnark-proof://{self.hash(proof)}'
})
return {
'proof': proof,
'public_inputs': public_inputs,
'proof_size_bytes': len(proof),
'proving_time_seconds': proof_time,
'proof_record': proof_record
}
async def verify_gradient_proof(self, proof, public_inputs):
"""
Verify ZK proof (fast: ~milliseconds)
"""
start_time = time.time()
is_valid = self.zksnark_verify(
verification_key=self.verification_key,
proof=proof,
public_inputs=public_inputs
)
verification_time = time.time() - start_time
# Create aéPiot verification record
verification_record = await self.aepiot_semantic.createBacklink({
'title': 'zk-SNARK Verification',
'description': f'Verification result: {is_valid}. Time: {verification_time*1000:.2f}ms',
'link': f'zksnark-verify://{self.hash(proof)}/{int(time.time())}'
})
return {
'valid': is_valid,
'verification_time_seconds': verification_time,
'verification_record': verification_record
}
async def federated_round_with_zk_verification(self, participants):
"""
Federated learning round where each gradient is ZK-verified
"""
verified_gradients = []
verification_records = []
for participant in participants:
# Participant computes gradients
gradients = participant.compute_gradients()
# Participant generates ZK proof
proof_result = await self.prove_gradient_correctness(
training_data=participant.local_data,
weights=participant.local_weights,
gradients=gradients
)
# Aggregator verifies proof
verification_result = await self.verify_gradient_proof(
proof=proof_result['proof'],
public_inputs=proof_result['public_inputs']
)
if verification_result['valid']:
verified_gradients.append(gradients)
verification_records.append(verification_result['verification_record'])
else:
print(f"Warning: Participant {participant.id} submitted invalid proof")
# Aggregate only verified gradients
if verified_gradients:
aggregated = np.mean(verified_gradients, axis=0)
# Create aéPiot aggregation record
agg_record = await self.aepiot_semantic.createBacklink({
'title': 'ZK-Verified Aggregation',
'description': f'Aggregated {len(verified_gradients)} ZK-verified gradients',
'link': f'zk-aggregate://{int(time.time())}'
})
return {
'aggregated_gradients': aggregated,
'num_verified': len(verified_gradients),
'verification_records': verification_records,
'aggregation_record': agg_record
}
else:
raise ValueError("No valid gradients received")4.2 Zero-Knowledge Range Proofs
Use Case: Prove gradients are within acceptable bounds without revealing exact values
python
class ZeroKnowledgeRangeProof:
"""
Bulletproofs for range proofs
Prove that gradient values are in acceptable range
"""
def __init__(self, min_value=-10.0, max_value=10.0):
self.min_value = min_value
self.max_value = max_value
self.aepiot_semantic = AePiotSemanticProcessor()
def generate_range_proof(self, value, min_val, max_val):
"""
Generate Bulletproof that value ∈ [min_val, max_val]
"""
from bulletproofs import RangeProof
# Convert to integer range (scale float)
scale = 1000
value_int = int(value * scale)
min_int = int(min_val * scale)
max_int = int(max_val * scale)
# Shift to positive range [0, max_int - min_int]
shifted_value = value_int - min_int
range_size = max_int - min_int
# Generate Bulletproof
proof = RangeProof.prove(
value=shifted_value,
min=0,
max=range_size,
blinding_factor=self.generate_random_blinding()
)
return proof
async def prove_gradient_in_range(self, gradients):
"""
Prove all gradient components are within acceptable range
"""
proofs = []
for gradient_value in gradients.flatten():
proof = self.generate_range_proof(
gradient_value,
self.min_value,
self.max_value
)
proofs.append(proof)
# Aggregate proofs (Bulletproofs are logarithmic in size)
aggregated_proof = self.aggregate_bulletproofs(proofs)
# Create aéPiot proof record
range_proof_record = await self.aepiot_semantic.createBacklink({
'title': 'Gradient Range Proof',
'description': f'Proved {len(gradients.flatten())} gradients in range [{self.min_value}, {self.max_value}]',
'link': f'range-proof://{int(time.time())}'
})
return {
'proof': aggregated_proof,
'num_gradients': len(gradients.flatten()),
'range': [self.min_value, self.max_value],
'proof_record': range_proof_record
}
def verify_range_proof(self, proof):
"""
Verify range proof (logarithmic verification time)
"""
from bulletproofs import RangeProof
is_valid = RangeProof.verify(proof)
return is_valid
async def gradient_clipping_with_zk_proof(self, gradients):
"""
Clip gradients and prove they're within bounds using ZK
"""
# Clip gradients
clipped = np.clip(gradients, self.min_value, self.max_value)
# Generate range proof
range_proof_result = await self.prove_gradient_in_range(clipped)
return {
'clipped_gradients': clipped,
'range_proof': range_proof_result['proof'],
'proof_record': range_proof_result['proof_record']
}4.3 Verifiable Computation with Trusted Execution Environments
Use Case: Hardware-based trusted computation for aggregation
python
class TEEFederatedAggregation:
"""
Use Intel SGX or ARM TrustZone for trusted aggregation
"""
def __init__(self, tee_type='sgx'):
self.tee_type = tee_type
self.aepiot_semantic = AePiotSemanticProcessor()
# Initialize TEE enclave
self.enclave = self.initialize_enclave()
def initialize_enclave(self):
"""
Initialize Trusted Execution Environment enclave
"""
if self.tee_type == 'sgx':
# Intel SGX initialization
from sgx import Enclave
enclave = Enclave(
enclave_path='./aggregation_enclave.so',
config_path='./enclave_config.xml'
)
return enclave
elif self.tee_type == 'trustzone':
# ARM TrustZone initialization
from trustzone import SecureWorld
secure_world = SecureWorld()
return secure_world
async def remote_attestation(self):
"""
Prove enclave is running genuine code on genuine hardware
"""
# Generate attestation quote
quote = self.enclave.generate_quote()
# Remote attestation with Intel Attestation Service (IAS)
attestation_result = await self.verify_with_ias(quote)
# Create aéPiot attestation record
attestation_record = await self.aepiot_semantic.createBacklink({
'title': 'TEE Remote Attestation',
'description': f'Attestation successful. Enclave verified as genuine.',
'link': f'tee-attestation://{int(time.time())}'
})
return {
'attestation_valid': attestation_result['valid'],
'enclave_measurement': attestation_result['mrenclave'],
'attestation_record': attestation_record
}
async def tee_secure_aggregation(self, encrypted_gradients):
"""
Aggregate gradients inside TEE enclave
"""
# 1. Remote attestation proves enclave is genuine
attestation = await self.remote_attestation()
if not attestation['attestation_valid']:
raise SecurityError("TEE attestation failed")
# 2. Participants send encrypted gradients to enclave
# Only enclave can decrypt (keys sealed to enclave)
# 3. Enclave decrypts and aggregates inside protected memory
aggregated = self.enclave.secure_aggregate(encrypted_gradients)
# 4. Enclave re-encrypts result for distribution
encrypted_result = self.enclave.encrypt_output(aggregated)
# 5. Create aéPiot TEE aggregation record
tee_record = await self.aepiot_semantic.createBacklink({
'title': 'TEE Secure Aggregation',
'description': f'Aggregated {len(encrypted_gradients)} gradients in SGX enclave',
'link': f'tee-aggregate://{int(time.time())}'
})
return {
'encrypted_aggregated': encrypted_result,
'enclave_measurement': attestation['enclave_measurement'],
'tee_record': tee_record
}
def enclave_code_example(self):
"""
Example of code running inside SGX enclave
This code has access to decrypted gradients but is isolated
"""
enclave_c_code = """
// This code runs inside SGX enclave
// Has access to decrypted data in protected memory
#include <sgx_tcrypto.h>
sgx_status_t ecall_aggregate_gradients(
const uint8_t* encrypted_gradients,
size_t num_gradients,
uint8_t* encrypted_result
) {
// Decrypt gradients inside enclave
float* gradients = decrypt_inside_enclave(encrypted_gradients, num_gradients);
// Aggregate (code verified by attestation)
float* aggregated = aggregate(gradients, num_gradients);
// Re-encrypt result
encrypt_inside_enclave(aggregated, encrypted_result);
// Securely erase decrypted data
memset_s(gradients, 0, sizeof(float) * num_gradients);
return SGX_SUCCESS;
}
"""
return enclave_c_code4.4 Zero-Knowledge Machine Learning (ZKML)
Cutting-Edge: Prove entire ML model execution using ZK
python
class ZeroKnowledgeMachineLearning:
"""
ZKML: Prove ML inference/training without revealing model or data
Extremely advanced - requires specialized ZK frameworks
"""
def __init__(self):
self.aepiot_semantic = AePiotSemanticProcessor()
async def prove_model_inference(self, model, input_data, predicted_output):
"""
Generate ZK proof that: output = model(input)
Without revealing model weights or input data
"""
# Convert ML model to arithmetic circuit
# This is extremely complex for deep neural networks
circuit = self.convert_model_to_circuit(model)
# Witness: model weights + input data + intermediate activations
witness = {
'weights': model.get_weights(),
'input': input_data,
'activations': self.compute_all_activations(model, input_data)
}
# Public input: only the predicted output
public_inputs = {
'output': predicted_output
}
# Generate proof (very computationally expensive)
proof = self.generate_zkml_proof(circuit, witness, public_inputs)
# Create aéPiot ZKML record
zkml_record = await self.aepiot_semantic.createBacklink({
'title': 'ZKML Inference Proof',
'description': f'Proved model inference without revealing weights or input',
'link': f'zkml://{int(time.time())}'
})
return {
'proof': proof,
'zkml_record': zkml_record
}