Federated Reinforcement Learning for Multi-agent Collaboration in Heterogeneous IoT Networks

FRL allows agents with distributed knowledge to cooperate in heterogeneous scenarios, which are characteristic of the Internet of Things, with very learning decisions emphasizing privacy considerations and cost-minimized communication. In addition, heterogeneous devices asynchronized updates and distributions render the scalability and convergence uneven as well as the lack of robustness. In this regard, the paper presents an Asynchronous Federated Reinforcement learning framework to address the problems such as stale updates through weighted aggregation, a communication-efficient model compression scheme, and reward shaping. Numerous experiments conducted in several artificial testbed IoT settings demonstrate that AFRL surpasses even the best synchronous FL methods and decentralized multi-agent RL baselines in terms of accomplishing high cumulative rewards with minimal communication cost as well as rapid convergence. Furthermore, the AFRL framework will retain its scalability for larger-sized IoT networks and robustness against a number of attacks and network perturbations, thus indicating that the current framework marks a very important step toward federated learning for real IoT systems while carefully tuning the balance between communication efficiency, learning accuracy, and system scalability. With this, this work fervently propels the vision for smart, privacy-aware, and robust multi-agent collaborations in the next generation of distributed edge computing ecosystems into the future.