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，系统集成分布式智能以驱动性能、自主性和实时适应性的趋势日益显著。联邦学习作为一项新兴关键技术脱颖而出，它能够在保护数据本地性的同时实现设备端模型训练。然而，其运行过程会带来巨大的能源与资源需求。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相对有限。为此，本文提出GreenFLag——一种旨在最小化电网能耗以完成联邦学习工作流、保障联邦学习模型性能，并通过将可再生能源接入系统来降低电网依赖的智能资源编排框架。GreenFLag采用软演员-评论家（Soft-Actor Critic）强化学习方法，在考虑通信竞争与可再生能源动态可用性的同时，对计算与通信资源进行联合优化。基于哥白尼（Copernicus）公开真实数据集的评估表明，与三种最先进的基线方法相比，GreenFLag平均减少94.8%的电网能耗，且主要依赖绿色电力运行。