Progressing toward a new generation of mobile networks, a clear focus on integrating distributed intelligence across the system is observed to drive performance, autonomy, and real-time adaptability. Federated learning (FL) stands out as a key emerging technique, enabling on-device model training while preserving data locality. However, its operation introduces substantial energy and resource demands. Energy needs are mostly met by grid power sources, while FL resource orchestration strategies remain limited. This work introduces GreenFLag, an agentic resource orchestration framework designed to minimize the energy consumption from the grid power to complete FL workflows, guarantee FL model performance, and reduce grid power reliance by incorporating renewable sources into the system. GreenFLag leverages a Soft-Actor Critic reinforcement learning approach to jointly optimize computational and communication resources, while accounting for communication contention and the dynamic availability of renewable energy. Evaluations using a real-world open dataset from Copernicus, demonstrate that GreenFLag significantly reduces grid energy consumption by 94.8% on average, compared to three state-of-the-art baselines, while primarily relying on green power.

翻译：随着新一代移动网络的发展，在系统中整合分布式智能以提升性能、自主性和实时适应性成为明确趋势。联邦学习作为一项关键新兴技术脱颖而出，可在保护数据本地性的同时实现设备端模型训练。然而，其运行会带来大量能耗和资源需求。当前能源需求主要依赖电网供电，而联邦学习的资源编排策略仍十分有限。本文提出GreenFLag——一种基于智能体的资源编排框架，旨在最大限度降低完成联邦学习工作流所需的电网能耗，在保证联邦学习模型性能的同时，通过将可再生能源引入系统来减少对电网供电的依赖。GreenFLag采用软演员-评论家强化学习方法，在考虑通信竞争和可再生能源动态可用性的前提下，联合优化计算与通信资源。基于哥白尼计划真实开放数据集的评估表明，与三种最先进基线方法相比，GreenFLag在主要依赖绿色能源的前提下，平均减少电网能耗94.8%。