Abstract

Federated learning (FL) has emerged as a viable paradigm for decentralized machine learning (DML) across multiple platforms while safeguarding data privacy. This study covers a thorough analysis of FL strategies intended to protect the privacy of data. It investigates the techniques and tactics FL uses to secure data privacy and explores the benefits and constraints of FL privacy protection. Using a methodical approach to the literature review, the study distinguishes FL approaches, explores the nuances of the FL transfer process, assesses current techniques, and identifies inherent vulnerabilities and shortcomings. These outcomes emphasize the vitality FL has for alleviating concerns about privacy while fostering collaborative learning. A variety of FL techniques are identified in the review, each of which contributes a distinct mechanism for maintaining privacy. These include differential privacy, homomorphic encryption, pruning, secure aggregation, secure multiparty computation, and zero-knowledge proofs, among others. This study provides scholars and practitioners with significant perspectives on existing procedures and prospective areas for advancement by integrating ideas from multiple sources to provide an overview of the current FL landscape concerning data privacy protection. The findings are more credible and reliable because of the systematic study, which also provides a strong basis for further research on FL and data privacy protection. At the end of the study, the implications of FL approaches for improving data privacy are covered. The significance of continuing research endeavors to tackle new problems and refine FL techniques for resilient and expandable privacy protection in the distributed machine learning age is underlined.

Keywords: Federated learning, Privacy preservation, Data privacy, Decentralized machine learning, Systematic review