Federated learning (FL) provides a promising collaborative framework to build a model from distributed clients, and this work investigates the carbon emission of the FL process. Cloud and edge servers hosting FL clients may exhibit diverse carbon footprints influenced by their geographical locations with varying power sources, offering opportunities to reduce carbon emissions by training local models with adaptive computations and communications. In this paper, we propose FedGreen, a carbon-aware FL approach to efficiently train models by adopting adaptive model sizes shared with clients based on their carbon profiles and locations using ordered dropout as a model compression technique. We theoretically analyze the trade-offs between the produced carbon emissions and the convergence accuracy, considering the carbon intensity discrepancy across countries to choose the parameters optimally. Empirical studies show that FedGreen can substantially reduce the carbon footprints of FL compared to the state-of-the-art while maintaining competitive model accuracy.

翻译：联邦学习（FL）为从分布式客户端构建模型提供了一种有前景的协作框架，本研究探讨了FL过程中的碳排放问题。托管FL客户端的云服务器和边缘服务器可能因地理位置不同、电力来源各异而呈现差异化的碳足迹，这为通过自适应计算与通信训练本地模型、减少碳排放提供了机遇。本文提出FedGreen——一种碳感知的联邦学习方法，该方法基于客户端的碳足迹特征与地理位置，利用有序丢弃（ordered dropout）作为模型压缩技术，向客户端共享自适应模型规模以高效训练模型。我们从理论上分析了产生的碳排放与收敛精度之间的权衡关系，并考虑各国碳强度差异以优化参数选择。实证研究表明，与现有先进方法相比，FedGreen能在保持竞争性模型精度的同时显著降低联邦学习的碳足迹。