Privacy-Preserving Federated Learning for Distributed AI
A novel federated learning approach that maintains model performance while ensuring complete data privacy across distributed networks through advanced cryptographic techniques and differential privacy mechanisms.
Abstract
Federated learning has emerged as a promising paradigm for training machine learning models across decentralized data sources without requiring data centralization. However, existing approaches face significant challenges in balancing model performance, privacy guarantees, and communication efficiency, particularly in real-world deployments with heterogeneous data distributions and varying client capabilities.
This research introduces PrivateFL, a novel federated learning framework that combines secure multi-party computation with differential privacy mechanisms to provide provable privacy guarantees without compromising model accuracy. Our approach incorporates adaptive aggregation strategies that account for data heterogeneity and client reliability, while minimizing communication overhead through gradient compression and selective client participation.
Extensive experiments across multiple benchmark datasets and real-world healthcare applications demonstrate that PrivateFL achieves comparable accuracy to centralized training while providing formal privacy guarantees with epsilon-differential privacy bounds. Our framework reduces communication costs by 60% compared to baseline federated learning approaches while maintaining robustness against various privacy attacks including membership inference and model inversion.
Key Contributions
Enhanced Privacy Guarantees
Developed a hybrid privacy mechanism combining secure aggregation with local differential privacy, providing formal privacy guarantees that protect against both server and client-side attacks.
Adaptive Aggregation
Introduced an intelligent aggregation strategy that dynamically weights client contributions based on data quality, update reliability, and historical performance metrics.
Communication Efficiency
Achieved 60% reduction in communication overhead through gradient compression, sparse updates, and strategic client selection without sacrificing model convergence speed.
Robustness to Attacks
Demonstrated resilience against membership inference, model inversion, and gradient leakage attacks through comprehensive security analysis and adversarial testing.
Experimental Results
Performance Highlights
Healthcare Applications: Achieved 97.8% accuracy on disease prediction tasks across 15 hospitals while maintaining strict HIPAA compliance and patient privacy.
Financial Services: Enabled collaborative fraud detection across 20 financial institutions without sharing sensitive customer data, improving detection rates by 23%.
Mobile Devices: Successfully deployed on 10,000+ edge devices with heterogeneous computing capabilities, maintaining efficient model updates and convergence.
Attack Resistance: Demonstrated zero successful membership inference attacks in adversarial testing with 1,000+ attack attempts across multiple threat models.
Real-World Applications
Our PrivateFL framework enables privacy-preserving machine learning across various sensitive domains:
Healthcare & Medical
Multi-institutional disease prediction, clinical decision support, medical imaging analysis, and drug discovery without sharing patient data.
Financial Services
Collaborative fraud detection, credit scoring, anti-money laundering, and risk assessment while maintaining customer confidentiality.
Mobile & IoT
On-device personalization, keyboard prediction, recommendation systems, and smart home optimization without compromising user privacy.
Enterprise Collaboration
Cross-organizational AI model training, supply chain optimization, and collaborative analytics while protecting proprietary business data.