Federated Learning (FL) distributes machine learning (ML) training across
many edge devices to reduce data transfer overhead and protect data privacy.
Since FL model training may span millions of devices and is thus
resource-intensive, prior work has focused on improving its resource efficiency
to optimize time-to-accuracy. However, prior work generally treats all
resources the same, while, in practice, they may incur widely different costs,
which instead motivates optimizing cost-to-accuracy. To address the problem, we
design CEFL, which uses adaptive cost-aware client selection policies to
optimize an arbitrary cost metric when training FL models. Our policies extend
and combine prior work on utility-based client selection and critical learning
periods by making them cost-aware. We demonstrate CEFL by designing
carbon-efficient FL, where energy's carbon-intensity is the cost, and show that
it i) reduces carbon emissions by 93\% and reduces training time by 50%
compared to random client selection and ii) reduces carbon emissions by 80%,
while only increasing training time by 38%, compared to a state-of-the-art
approach that optimizes training time.

Federated Learning 通过自适应成本感知的客户端选择策略优化任意成本指标，将资源效率提高到时间 - 准确性之间的最优化问题解决掉，并通过设计碳效率的 Federated Learning 模型，以其能耗的碳排放强度为成本，在减少碳排放和训练时间方面具有显著的优势。