Swarm-Augmented Federated Reinforcement Learning for Resilient Edge Energy Networks: Adaptive Topology, Distributed Optimization, and Self-Healing Microgrid Coordination

Abstract

Federated reinforcement learning has emerged as a compelling paradigm for privacy-preserving distributed energy management, enabling microgrid agents to learn collaborative dispatch and trading policies without sharing raw consumption or generation data across institutional boundaries. A persistent limitation of existing federated energy systems, however, is their reliance on fixed, pre-configured communication topologies for gradient aggregation: when microgrid nodes fail, join, or experience link degradation, the federated network either fails entirely or requires manual reconfiguration that is incompatible with the autonomous operation that distributed energy systems require. This paper proposes a swarm-augmented federated reinforcement learning architecture that integrates bio-inspired adaptive topology management with a privacy-preserving federated reinforcement learning policy optimization framework for distributed microgrid energy trading. The proposed system employs stigmergic path reinforcement mechanisms to dynamically discover and maintain efficient gradient communication paths between federated agents as network topology changes, enabling the federated energy system to self-heal following node failures without administrator intervention. A reputation-weighted policy aggregation mechanism further improves resilience by detecting and down-weighting adversarial gradient submissions. Simulation across five operational scenarios, including normal operation, single and dual node failure, node rejoining, and adversarial gradient injection, demonstrates that the proposed architecture recovers from single node failure within one to two federated rounds at 91 percent of the optimal dispatch efficiency, compared to 0 percent for fixed topology baselines that cannot recover without manual reconfiguration.