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.

FedGreen 是一种碳感知的联邦学习方法，通过采用基于客户端的碳排放数据和位置的自适应模型大小训练技巧，以及使用有序的随机失活作为模型压缩技术，可以有效地训练模型，并在考虑不同国家碳强度差异的同时，降低碳排放，并保持竞争性的模型准确性。