Characterizing Carbon Cost of Federated Learning

Federated learning (FL) is a decentralized learning approach for training machine learning models without sharing user data with a centralized server. Though FL is considered as a practical solution to mitigate the risk of privacy leakage in training, its environmental impact can be significant, especially considering the scale of billions of mobile users. In this letter, we first demonstrate the carbon cost of privacy by quantifying and characterizing the carbon footprint (CF) of FL while accounting for both server-side FL settings and client heterogeneity. Our analysis reveals that CF-optimal FL settings vary by the service-level objective, and client heterogeneity further complicates CF optimization of FL. We believe our work will be a practical guideline for designing carbon-efficient FL systems.