Work

Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling

Year: 2025

Type: conference-paper

Abstract: Training large-scale machine learning models incurs substantial carbon emissions. Federated Learning (FL), by distributing computation across geographically dispersed clients, offers a natural framework to leverage regional and temporal variations in Carbon Intensity (CI). This paper investigates how to reduce emissions in FL through carbon-aware client selection and training scheduling. We first quantify the emission savings of a carbon-aware scheduling policy that leverages slack time-permitting a modest extension of the training duration so that clients can defer local training rounds to lower-carbon periods. We then examine the performance trade-offs of such scheduling which stem from statistical heterogeneity among clients, selection bias in participation, and temporal correlation in model updates. To leverage these trade-offs, we construct a carbon-aware scheduler that integrates slack time, $\alpha$-fair carbon allocation, and a global fine-tuning phase. Experiments on realworld CI data show that our scheduler outperforms slackagnostic baselines, achieving higher model accuracy across a wide range of carbon budgets, with especially strong gains under tight carbon constraints.

Source: Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems

DOI: 10.1109/mascots67699.2025.11283314

Authors: Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia

Institutions: Centre Inria d'Université Côte d'Azur, Linköping University

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funder: Agence Nationale de la Recherche