Federated Learning and Data Privacy: A Review of Challenges and Opportunities

Praveen Kumar Myakala, Chiranjeevi Bura, Anil Kumar Jonnalagadda

Published: 2024/12/10

Published in: International Journal of Research Publication and Reviews

Abstract

This article provides an extensive review of the challenges and opportunities at the intersection of federated learning (FL) and data privacy. Federated learning is a distributed machine learning paradigm enabling collaborative model training across decentralized devices without transferring raw data to a central repository. This method reduces privacy risks and aligns with regulatory compliance while unlocking potential in sensitive domains such as healthcare, finance, and IoT. Despite these advantages, FL faces critical challenges, including susceptibility to adversarial attacks, communication bottlenecks, heterogeneity in devices and data distributions, and limited privacy guarantees. Promising research directions include the integration of differential privacy, secure multi-party computation, and blockchain for enhanced security. This paper underscores the importance of interdisciplinary efforts to overcome these challenges and explores potential applications across domains like personalized medicine, smart grid optimization, and decentralized AI in edge computing environments. It concludes by outlining pathways for future research, emphasizing the need for scalable, efficient, and privacy-preserving FL architectures.

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