Federated Meta-Reinforcement Learning for Adaptive Wireless Networks: A Unified Framework Integrating Proximal Policy Optimization, Meta-Learning, and Graph Neural Networks

Proposed Federated Meta-Reinforcement Learning (FMRL) integrates PPO, meta-learning, and graph neural networks (GNNs) into a privacy-preserving distributed framework for adaptive wireless networks. By deploying localized PPO agents and a federated meta-learner, FMRL enables rapid adaptation to dynamic conditions (e.g., traffic fluctuations, SNR variations) while GNNs model spatial dependencies to manage interference. Leveraging Transformer and GraphSAGE, FMRL optimizes power/channel allocation without centralized data aggregation, outperforming traditional methods. This unified system uniquely combines federated learning, meta-learning, and GNNs, ensuring scalable, privacy-aware network optimization.