Dynamic federated multi-agent planning under partial observability and network instability for real-time edge applications

Abstract In recent years, the proliferation of autonomous systems at the edge—including robotic swarms, autonomous vehicles, and intelligent sensor networks—has highlighted the urgent need for resilient multi-agent planning under uncertain, decentralized conditions. Traditional multi-agent planning architectures typically assume stable communication links and complete observability, conditions that are rarely met in real-world deployments marked by dynamic network topologies, delayed information exchange, and partial observability. This paper introduces Dynamic Federated Multi-Agent Planning (DFMAP), a novel framework that integrates federated reinforcement learning (FRL) with partially observable Markov decision processes (POMDPs) for real-time decision-making in volatile edge environments. The proposed architecture enables agents to learn locally, share model updates without centralized coordination, and collaboratively build global planning policies even under intermittent connectivity. A probabilistic graph-based model tracks dynamic communication topology shifts, while an asynchronous aggregation strategy with staleness correction ensures robustness to network instability. Experiments conducted in both simulated and semi-realistic edge environments—covering decentralized robotic navigation and collaborative search—demonstrate DFMAP’s consistent superiority over centralized and naively decentralized baselines. Specifically, DFMAP achieves a task success rate of 92.5%, a cumulative reward of 121.8 ± 5.4 by training round 100, and a per-round communication overhead of only 15.2 MB, representing more than a 66% reduction relative to the centralized baseline. Under severe communication stress with 50% link dropout, reward degradation is held to just 11.0%, substantially below the 22.4% and 30.0% observed for competing methods.