Emerging Paradigms for Securing Federated Learning Systems
This is an incremental survey paper that identifies promising technologies for improving secure Federated Learning systems for IoT and privacy-sensitive applications.
This survey examines emerging approaches like Trusted Execution Environments and Quantum Computing to enhance privacy and efficiency in Federated Learning systems, which currently face high computational costs and limited scalability with existing methods.
Federated Learning (FL) facilitates collaborative model training while keeping raw data decentralized, making it a conduit for leveraging the power of IoT devices while maintaining privacy of the locally collected data. However, existing privacy- preserving techniques present notable hurdles. Methods such as Multi-Party Computation (MPC), Homomorphic Encryption (HE), and Differential Privacy (DP) often incur high compu- tational costs and suffer from limited scalability. This survey examines emerging approaches that hold promise for enhancing both privacy and efficiency in FL, including Trusted Execution Environments (TEEs), Physical Unclonable Functions (PUFs), Quantum Computing (QC), Chaos-Based Encryption (CBE), Neuromorphic Computing (NC), and Swarm Intelligence (SI). For each paradigm, we assess its relevance to the FL pipeline, outlining its strengths, limitations, and practical considerations. We conclude by highlighting open challenges and prospective research avenues, offering a detailed roadmap for advancing secure and scalable FL systems.