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

• URL: http://arxiv.org/abs/2509.08980v1
• Date: Wed, 10 Sep 2025 20:24:26 GMT
• Title: Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling
• Authors: Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia,
• Abstract summary: Training large-scale machine learning models incurs substantial carbon emissions.<n>This paper investigates how to reduce emissions in Federated Learning through carbon-aware client selection and training scheduling.
• Score: 8.491852197957849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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