Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL, its environmental impact is still under investigation, since several critical challenges regarding its applicability to wireless networks have been identified. Towards mitigating the carbon footprint of FL, the current work proposes a Genetic Algorithm (GA) approach, targeting the minimization of both the overall energy consumption of an FL process and any unnecessary resource utilization, by orchestrating the computational and communication resources of the involved devices, while guaranteeing a certain FL model performance target. A penalty function is introduced in the offline phase of the GA that penalizes the strategies that violate the constraints of the environment, ensuring a safe GA process. Evaluation results show the effectiveness of the proposed scheme compared to two state-of-the-art baseline solutions, achieving a decrease of up to 83% in the total energy consumption.

翻译：联邦学习（FL）作为一种去中心化技术应运而生，与传统集中式方法不同，设备以协作方式进行模型训练，同时保护数据隐私。尽管联邦学习已取得现有成果，但其环境影响仍在研究中，因为已识别出若干关于其在无线网络适用性的关键挑战。为减少联邦学习的碳足迹，本文提出一种遗传算法（GA）方法，旨在通过协调所涉及设备的计算与通信资源，在保证特定联邦学习模型性能目标的前提下，最小化联邦学习过程的整体能耗及任何不必要的资源利用。在遗传算法的离线阶段引入了惩罚函数，该函数会对违反环境约束的策略进行惩罚，从而确保遗传算法的安全执行。评估结果表明，与两种现有基线解决方案相比，所提方案具有有效性，总能耗降低幅度高达83%。