FEDERATED REINFORCEMENT LEARNING FOR INTELLIGENT RESOURCE ALLOCATION IN NEXT-GENERATION WIRELESS NETWORKS

The rapid expansion of sixth-generation (6G) communication technologies, edge computing, and Internet of Things (IoT) ecosystems has created unprecedented challenges in wireless resource management. Conventional centralized optimization techniques often struggle to satisfy the stringent latency, scalability, and privacy requirements of modern communication networks. Federated Reinforcement Learning (FRL) has emerged as a promising solution by enabling distributed model training without exposing raw user data while simultaneously learning optimal resource allocation policies through interaction with dynamic environments. This paper proposes a federated reinforcement learning framework for intelligent resource allocation in next-generation wireless networks. The proposed architecture integrates edge intelligence, federated aggregation, and deep reinforcement learning to optimize bandwidth distribution, spectrum utilization, computational offloading, and energy efficiency. Experimental evaluation demonstrates that the proposed framework significantly improves throughput, reduces latency, lowers communication overhead, and enhances network fairness compared with conventional centralized learning approaches. The study illustrates the growing importance of distributed artificial intelligence in future wireless communication infrastructures.