Federated learning: Challenges and future work
1 Department of Information and Communication Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh.
2 Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Review Article
World Journal of Advanced Research and Reviews, 2022, 15(02), 850-862
Publication history:
Received on 12 June 2022; revised on 21 August 2022; accepted on 29 August 2022
Abstract:
This paper provides a comprehensive survey of Federated Learning (FL), an emerging paradigm in machine learning that allows multiple clients such as mobile devices or distributed data centers to collaboratively train shared models without exchanging raw data. By localizing data and transmitting only model updates, FL ensures data privacy, enhances security, and reduces the risks and costs associated with traditional centralized learning methods. The paper analyzes FL from five key dimensions: data partitioning strategies, privacy-preserving mechanisms, machine learning models, communication architectures, and system heterogeneity. In addition to exploring foundational concepts, the paper highlights enabling technologies and platforms that support FL, reviews widely used protocols, and presents real-world applications across industries such as healthcare, finance, and IoT. The authors also delve into the challenges of deploying FL in heterogeneous and large-scale environments, including issues related to communication efficiency, device reliability, and algorithmic fairness. Finally, the survey outlines open research directions and provides practical insights to help data scientists and engineers design more robust and privacy-preserving FL systems suitable for critical real-world deployments.
Keywords:
Federated learning; Machine learning; Privacy protection; Personalized federated learning
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0