Adaptive Federated Learning with Lyapunov Optimization for Robust Radio Link Failure Detection in 5G Networks

Radio Link Failure (RLF) detection is essential for maintaining reliable connectivity in 5G networks. However, traditional centralized detection mechanisms often encounter scalability and latency constraints when managing large-scale, geographically distributed infrastructures. To address this challenge, we introduce a Lyapunov-driven federated learning framework that adaptively selects gNodeBs based on both data utility and historical participation. This approach leverages an LSTM-based local model to capture temporal patterns in link performance, thereby enhancing RLF detection. Extensive evaluations on a real-world 5G dataset demonstrate that the proposed method achieves superior performance compared to baseline approaches when detecting rare failure events. By simultaneously prioritizing performance and fairness, this framework offers a scalable solution suited to diverse and dynamic 5G environments.