Federated Learning for 6G Networks: A Comprehensive Review of Challenges, Techniques, and Future Directions

Abstract

The convergence of Federated Learning (FL) and sixth-generation (6G) communication systems promise to revolutionize distributed intelligence by addressing emerging demands for data privacy, real-time processing, and massive connectivity. However, integrating FL within 6G introduces complex challenges ranging from heterogeneous data and devices to communication bottlenecks, energy constraints, and stringent security demands. This review provides a comprehensive examination of FL techniques and their applicability in terms of 6G communication models. The review also emphasizes how these technologies are used in real-world fields like healthcare, autonomous systems, and digital twins—areas where privacy, reliability, and latency are mission-critical. Unlike earlier surveys that treat FL and 6G as separate research tracks, this paper critically reviews their convergence, identifying how FL techniques must evolve to meet the architectural, functional, and regulatory demands of 6G systems. It discusses ongoing challenges and emerging directions such as quantum-safe protocols, interpretable federated learning, and energy-aware orchestration. By synthesizing cross disciplinary insights and mapping current gaps, this review aims to guide future research in developing robust, adaptive, and secure FL frameworks.