REVIEW article
Front. Comput. Sci.
Sec. Theoretical Computer Science
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1617597
Deep Federated Learning: A Systematic Review of Methods, Applications and Challenges
Provisionally accepted- Informatics Institute of Technology, Colombo, Sri Lanka
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Federated Learning (FL) represents a paradigm shift in machine learning, enabling collaborative model training on decentralized data while preserving user privacy. However, the transition from theory to real-world application is impeded by significant challenges, including high communication costs, statistical and system heterogeneity and persistent privacy vulnerabilities. These barriers critically limit the performance, scalability and security of FL systems. This paper provides a systematic review of the state-of-the-art solutions developed to address these fundamental obstacles. The review analyzes core methodological advancements, including advanced model aggregation methods, techniques to enhance communication efficiency such as model compression and decentralized training and strategies to combat statistical heterogeneity arising from non-IID data. Furthermore, it delves into emerging paradigms like Federated Meta-Learning and Federated Reinforcement Learning, alongside advanced architectural models such as hierarchical and blockchain-based systems. The practical impact of these advancements is contextualized through a review of key application domains, including healthcare, vehicular networks and the Internet of Things. A benchmark analysis is presented to assess the practical efficacy of these diverse techniques. In conclusion, this work synthesizes the critical trade-offs inherent in FL systems and highlights key directions for future research, offering a comprehensive guide for researchers and practitioners in this rapidly evolving field.
Keywords: Federated learning, Model aggregation, Communication efficiency, Statistical heterogeneity, Privacy, meta-learning, reinforcement learning
Received: 24 Apr 2025; Accepted: 09 Oct 2025.
Copyright: © 2025 Cooray, Sendanayake, Vithanaarachchi and Yapa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lakshan Anjana Cooray, lakshan.20221470@iit.ac.lk
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