Deep federated learning: a systematic review of methods, applications, and challenges

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.