  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.
