Encrypted Federated Learning: Techniques and Applications

Abstract

Encrypted Federated Learning (EFL) represents a pivotal advancement in the realm of privacy-preserving machine learning. This technique combines the principles of federated learning with advanced cryptographic protocols to enable secure and confidential model training across decentralized networks. By encrypting data contributions from multiple devices or institutions before aggregation, EFL mitigates the risks associated with data exposure and unauthorized access, thus fostering trust among participants. This paper provides an in-depth exploration of various techniques employed in EFL, including homomorphic encryption, secure multiparty computation (MPC), and differential privacy. We examine their applicability in different scenarios, ranging from healthcare and finance to industrial IoT, highlighting their respective strengths and trade-offs. Furthermore, we discuss practical considerations such as computational overhead, communication efficiency, and scalability challenges that influence the adoption of EFL in real-world applications. Through case studies and experimental evaluations, we demonstrate the efficacy of EFL in preserving data privacy while achieving competitive model accuracy compared to conventional federated learning approaches. Finally, we identify emerging research directions and potential avenues for optimizing EFL frameworks to address evolving security and performance requirements in distributed machine learning environments.