Federated Learning (FL) is a decentralized machine learning approach where local models are trained on distributed clients, allowing privacy-preserving collaboration by sharing model updates instead of raw data. However, the added communication overhead and increased training time caused by heterogenous data distributions results in higher energy consumption and carbon emissions for achieving similar model performance than traditional machine learning. At the same time, efficient usage of available energy is an important requirement for battery constrained devices. Because of this, many different approaches on energy-efficient and carbon-efficient FL scheduling and client selection have been published in recent years. However, most of this research oversimplifies power performance characteristics of clients by assuming that they always require the same amount of energy per processed sample throughout training. This overlooks real-world effects arising from operating devices under different power modes or the side effects of running other workloads in parallel. In this work, we take a first look on the impact of such factors and discuss how better power-performance estimates can improve energy-efficient and carbon-efficient FL scheduling.

翻译：联邦学习（FL）是一种去中心化机器学习方法，其中本地模型在分布式客户端上训练，通过共享模型更新而非原始数据实现隐私保护协作。然而，由异构数据分布带来的额外通信开销和训练时间增加，导致在达到与机器学习类似模型性能时产生更高的能耗和碳排放。同时，高效利用可用能源是电池受限设备的重要要求。因此，近年来涌现出许多关于能效和碳效FL调度与客户端选择的不同方法。然而，大多数研究过度简化了客户端的功耗性能特征，假设其在训练过程中处理每个样本始终消耗相同能量。这忽视了设备在不同功耗模式下运行产生的现实影响，或并行运行其他工作负载的副作用。在本工作中，我们初步探讨了这些因素的作用，并讨论了更精准的功耗性能估算如何提升能效与碳效FL调度。