提出异步联邦强化学习框架，优化边缘计算任务卸载。
FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems
- 采用异步联邦架构，本地智能体学节点特性，全局评论家协同优化。
- 在PeersimGym中任务丢失率和延迟均优于基线方法。
- 适合动态边缘环境下的分布式任务调度系统研究者。
边缘计算通过去中心化架构将计算资源靠近终端用户，应对连接设备网络日益增长的数据需求。这种去中心化挑战了传统全集中式编排的模式，后者存在延迟高、资源瓶颈等问题。本文提出 extbf{FAuNO}—— extit{联邦异步网络编排器}，一种基于缓冲的异步联邦强化学习（FRL）框架，用于边缘系统的去中心化任务卸载。FAuNO采用演员-评论家架构，本地演员学习节点特性和节点间交互，联邦评论家聚合各智能体经验，促进高效协作并提升整体系统性能。在 extit{PeersimGym}环境中的实验表明，FAuNO在降低任务丢失率和延迟方面持续达到或超过启发式算法及联邦多智能体强化学习基线，凸显其在动态边缘计算场景中的适应能力。
原文摘要 · Abstract (English)
Edge computing addresses the growing data demands of connected-device networks by placing computational resources closer to end users through decentralized infrastructures. This decentralization challenges traditional, fully centralized orchestration, which suffers from latency and resource bottlenecks. We present \textbf{FAuNO} -- \emph{Federated Asynchronous Network Orchestrator} -- a buffered, asynchronous \emph{federated reinforcement-learning} (FRL) framework for decentralized task offloading in edge systems. FAuNO adopts an actor-critic architecture in which local actors learn node-specific dynamics and peer interactions, while a federated critic aggregates experience across agents to encourage efficient cooperation and improve overall system performance. Experiments in the \emph{PeersimGym} environment show that FAuNO consistently matches or exceeds heuristic and federated multi-agent RL baselines in reducing task loss and latency, underscoring its adaptability to dynamic edge-computing scenarios.
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