Could Model Partitioning Make Federated Learning More Sustainable?

Tobias Frohlich; Tiffany Vlaar; Lauritz Thamsen

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arXiv · 2608.14242

Could Model Partitioning Make Federated Learning More Sustainable?

Tobias Frohlich·Tiffany Vlaar·Lauritz Thamsen

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Abstract

As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint has become a growing concern. Addressing this, methods for sustainable FL align training with low-carbon energy availability or low grid demand and reduce the energy consumption of clients powered by high-carbon sources by decreasing the size of their models. We propose applying model partitioning, which can shift energy consumption by offloading parts of a model to another participant, in response to carbon- or grid-aware signals. Our preliminary findings show that for some partition points, model partitioning can reduce a participant's energy consumption by up to 76% without any significant time or energy consumption overhead compared to non-partitioned training.

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Tobias Frohlich, Tiffany Vlaar, Lauritz Thamsen. 2026-08-14. Could Model Partitioning Make Federated Learning More Sustainable?. https://arxiv.org/abs/2608.14242

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