Privacy-Preserving Federated Learning: A Comparative Study of Techniques and their Practical Implementations

Abstract: Federated learning (FL) has emerged as a revolutionary solution to decentralized machine learning that provides model training over many clients without sharing raw data. Still, privacy threats continue to be a significant challenge because of possible loopholes in data aggregation, adversarial attacks, and communication schemes [1]. This article critically compares some of the privacy-preserving methods applied in FL, such as differential privacy, secure multi-party computation, homomorphic encryption, clustered sampling, and robust aggregation. By considering their efficacy, computational overheads, and trade-offs between model utility and privacy, this research identifies important advantages and shortcomings of each approach. Additionally, the paper delves into open challenges and outlines future directions for research to improve privacy in FL, especially in edge computing and 6G-enabled IoT settings