Advancements in Privacy-Preserving Techniques for Federated Learning: A Machine Learning Perspective

Abstract

Federated learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. However, concerns about data privacy remain significant, particularly in scenarios where sensitive information is involved. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine learning perspective. It categorizes and analyses state-of-the-art approaches within a unified framework, highlighting their strengths, limitations, and potential applications. By providing insights into the landscape of privacy-preserving federated learning, this review aims to guide researchers and practitioners in developing robust and privacy-conscious machine learning solutions for collaborative environments. The paper concludes with future research directions to address ongoing challenges and further enhance the effectiveness and scalability of privacy-preserving federated learning.