Enabling Privacy-Preserving Machine Learning: Federal Learning with Homomorphic Encryption

Abstract

In order to protect the cooperative training of machine learning models across decentralized devices, this study presents a novel privacy-preserving federated learning system with homomorphic encryption (PFed-HE). Starting with the careful selection and improvement of homomorphic encryption algorithms for effective and safe computations, the methodology takes a multifaceted approach. A unique approach for encrypting and aggregating gradients while maintaining privacy is introduced, utilizing homomorphic encryption and incorporating differential privacy techniques for an additional layer of confidentiality. The federated learning architecture includes a client-side encryption module, which smoothly integrates the encryption process into the model training workflow. A decentralized model aggregation approach allows encrypted model updates from numerous clients to be securely integrated while maintaining individual data privacy. To improve security, the methodology incorporates a dynamic key management system with periodic key rotation and a secure key agreement protocol for establishing shared encryption keys. Batch processing, parallelizing homomorphic encryption processes, and communication compression are the main tactics used in performance optimization to reduce computational overhead and improve scalability. The PFed-HE system is integrated with prominent federated learning frameworks, thoroughly evaluated in simulated real-world scenarios, and applied to specific use cases in fields such as healthcare and finance. This PFed-HE system tackles the ethical issues and openness that are critical for deployment in sensitive applications, in addition to showcasing advances in privacy-preserving machine learning.