Comprehensive Review on Privacy-Preserving Machine Learning Techniques for Exploring Federated Learning

Abstract

In the rapidly evolving field of machine learning, federated learning has emerged as a pivotal approach for enabling collaborative model training across decentralized data sources while maintaining data privacy. This comprehensive review explores various privacy-preserving techniques within the context of federated learning, offering a detailed examination of their mechanisms, effectiveness, and application domains. The review begins by providing a foundational overview of federated learning and its significance in protecting data privacy. It then delves into an array of privacy-preserving strategies, including differential privacy, secure multi-party computation, homomorphic encryption, and federated learning-specific enhancements such as noise addition and aggregation protocols. The review critically analyzes the strengths and limitations of these techniques, evaluates their performance in real-world scenarios, and identifies emerging trends and future research directions. By synthesizing current knowledge and advancements, this paper aims to serve as a valuable resource for researchers and practitioners seeking to understand and implement privacy-preserving methods in federated learning systems.