ML-driven latency optimization for mobile edge computing in fiber-wireless access networks.

The increasing quest for ultra-low latency in mobile edge computing (MEC) over fiber-wireless networks pose challenges in adapting to real-time demands under resource constraints. Congestion-prone methods of centralized learning and static routing waste resources. A machine learning-based latency optimization framework is proposed that integrates multiple advanced paradigms for proactive traffic management, intelligent routing, and efficient task offloading while ensuring data privacy. The framework encompasses self-supervised learning, federated learning, spatiotemporal graph neural networks (GNN), adaptive multi-agent reinforcement learning, and hypergraph transformers. These tools are useful for dynamic congestion-aware routing, accurate spatiotemporal traffic prediction, and optimal resource slicing for latency-sensitive applications. Hypergraph transformers enable dynamic allocation of resources across network slices. The experiments shows significant performance improvements, 34-42 % reduction in end-to-end latency, 29-35 % faster task execution, 50 % less training time, better traffic prediction accuracy, and lower slice switching delays. This framework can underpin some of the most crucial low-latency applications like augmented reality and autonomous driving, providing a powerful solution for the next generation of MEC networks working under stringent performance and privacy constraints.•Dynamic congestion-aware routing using adaptive multi-agent reinforcement learning•Proactive traffic prediction using spatiotemporal GNN•Federated, self-supervised learning to maximize resource slicing and delay-aware task offloading.