Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
This work addresses the environmental impact of machine learning training for researchers and practitioners, but it is incremental as it builds on existing FL frameworks with carbon-aware optimizations.
The paper tackled the problem of reducing carbon emissions in Federated Learning by proposing a carbon-aware scheduler that leverages slack time, fair carbon allocation, and fine-tuning, resulting in higher model accuracy across various carbon budgets, with strong gains under tight constraints.
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, $α$-fair carbon allocation, and a global fine-tuning phase. Experiments on real-world CI data show that our scheduler outperforms slack-agnostic baselines, achieving higher model accuracy across a wide range of carbon budgets, with especially strong gains under tight carbon constraints.