CLOVER: Carbon Optimization of Federated Learning over Heterogeneous Clients

Chanwoo Cho, Yonglak Son, Seongbin Park, Young Geun Kim

Abstract: Federated Learning (FL) is a decentralized approach to train a DNN model without sharing the on-device training samples with a cloud server. Although FL is a practical solution to prevent the privacy leakage in DNN training, the environmental impact of FL can be significant given billions of mobile users. However, optimizing carbon emissions of FL is challenging because of its unique features such as heterogeneous carbon intensity, system/data heterogeneity, and network variability. In this paper, we propose a carbon-aware FL algorithm ---CLOVER--- which enables carbon efficient selections of participants and their respective training samples considering the aforementioned features. In our experiments with various combinations of DNN models and datasets, CLOVER improves the FL carbon efficiency by 25.0%, on average, while still guaranteeing the convergence with better accuracy.

External IDs:dblp:conf/islped/ChoSPK24