SecureFL: Privacy-Preserving Federated Learning Framework

📄 Research Paper

Title: SecureFL: A Privacy-Preserving Federated Learning Framework with Differential Privacy

Authors: Li Xiaoming, Wang Jing, Zhang Qiwei

Conference: NeurIPS 2024 (Neural Information Processing Systems)

Abstract: We propose SecureFL, a federated learning framework that provides strong privacy guarantees through differential privacy and secure aggregation. Our system enables organizations to collaboratively train models while keeping their data local and private.

Key Contributions:

Novel differential privacy mechanism for federated learning
Secure aggregation protocol with Byzantine fault tolerance
30% reduction in communication overhead
Formal privacy analysis and guarantees

🔒 Privacy & Performance

ε=0.1

Differential privacy guarantee achieved

30%

Reduction in communication cost

95%

Model accuracy preserved

🛡️ Security Features

Comprehensive privacy protection and security mechanisms for federated learning.

Differential Privacy

Mathematical privacy guarantees with configurable privacy budget.

Secure Aggregation

Cryptographic protocols to protect individual model updates.

Byzantine Fault Tolerance

Robust against malicious participants and data poisoning attacks.

Data Locality

Training data never leaves the participant’s local environment.

Communication Optimization

Compressed gradients and adaptive communication scheduling.

Audit Trail

Comprehensive logging for compliance and reproducibility.

🔬 Technical Evaluation

Privacy Analysis

Our framework provides formal privacy guarantees through:

Local Differential Privacy: Each participant adds calibrated noise
Global Privacy Budget: Carefully managed across training rounds
Privacy Accounting: Precise tracking of privacy loss over time

Performance Benchmarks

Dataset	Centralized Accuracy	Federated Accuracy	Privacy Level
CIFAR-10	94.2%	92.8%	ε=1.0
MNIST	99.1%	98.7%	ε=0.5
IMDB	89.3%	87.9%	ε=0.1
Medical Images	91.7%	90.2%	ε=0.05

Communication Efficiency

Our optimization techniques significantly reduce communication overhead:

Gradient Compression: 5x reduction in message size
Adaptive Scheduling: 40% fewer communication rounds
Partial Participation: Scalable to thousands of participants

Security Evaluation

Extensive testing against various attack scenarios:

Model Inversion Attacks: Successfully defended with <0.1% information leakage
Membership Inference: Privacy guarantee holds under worst-case assumptions
Byzantine Attacks: System remains functional with up to 25% malicious participants

🔧 Framework Usage

Quick Start Guide

Get started with SecureFL in just a few lines of code:

from securefl import FederatedTrainer, PrivacyConfig

# Configure privacy settings
privacy_config = PrivacyConfig(
    epsilon=1.0,           # Privacy budget
    delta=1e-5,            # Privacy delta
    clipping_norm=1.0      # Gradient clipping
)

# Initialize federated trainer
trainer = FederatedTrainer(
    model=your_model,
    privacy_config=privacy_config,
    aggregation='secagg',   # Secure aggregation
    byzantine_tolerance=True
)

# Start federated training
trainer.fit(
    participants=client_list,
    rounds=100,
    local_epochs=5
)

Deployment Architecture

SecureFL supports multiple deployment scenarios:

Cross-silo Federation: Banks, hospitals, companies
Cross-device Federation: Mobile devices, IoT sensors
Hybrid Federation: Mixed organizational and device participants

Integration Examples

Healthcare Collaboration

# Multi-hospital medical imaging model
hospitals = ['hospital_a', 'hospital_b', 'hospital_c']
model = MedicalImageClassifier()

federated_model = trainer.train_medical_model(
    participants=hospitals,
    privacy_budget=0.1,  # Strict privacy for medical data
    compliance='HIPAA'
)

Financial Fraud Detection

# Multi-bank fraud detection
banks = ['bank_1', 'bank_2', 'bank_3']
model = FraudDetectionModel()

fraud_model = trainer.train_fraud_detector(
    participants=banks,
    privacy_budget=0.5,
    regulatory_compliance='GDPR'
)

Start Secure Collaboration

Ready to enable privacy-preserving machine learning collaboration? Deploy SecureFL in your organization.

Get SecureFL