Federated Learning: Challenges, Methods, and Future Directions

Dear Colleagues,

Federated Learning (FL) has emerged as a transformative decentralized machine learning paradigm, enabling collaborative model training across distributed devices while rigorously preserving data privacy. This Special Issue delves into the critical challenges, innovative methodologies, and future prospects of FL, focusing on core issues such as data heterogeneity, communication efficiency, privacy preservation, and security threats. Featured contributions present cutting-edge techniques, including efficient model aggregation, robustness enhancement, and differential privacy mechanisms, to advance FL performance.

This Special Issue examines FL’s practical applications in domains including healthcare, IoT, and edge computing, emphasizing scalability and fairness in real-world deployments. It also highlights promising research directions, such as improving algorithmic robustness and fairness, reducing computational overhead, and integrating FL with emerging technologies such as blockchain and 6G networks.

By curating state-of-the-art research, this Special Issue supports FL as a scalable, efficient, and privacy-preserving cornerstone for next-generation AI systems.

Dr. Naiyue Chen
Dr. Ivan Serina
Guest Editors

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