Multi-layer Collaborative Federated Reinforcement Learning for Interference Management in Distributed IIoT Networks

In industrial internet of things (IIoT) scenarios, the dense deployment of numerous wireless devices coupled with limited spectrum resources leads to severe interference problems. In addition, the data isolation and transmission limitation in industrial scenarios hinder the direct interaction of large amounts of raw data, making it difficult to take advantage of intelligent interference management based on artificial intelligence (AI). We propose an interference coordination approach based on Multi-layer Collaborative Federated Reinforcement Learning (MCFRL), which not only achieves environment-adaptive intelligent interference management but also addresses the issue of restricted cross-domain data sharing. Simulation results show that the proposed approach can effectively improve the throughput performance of IIoT networks and reduce energy and latency costs through flexible deployment adjustments.