EcoLearn: Optimizing the Carbon Footprint of Federated Learning

TL;DR

EcoLearn is a novel federated learning approach that reduces carbon emissions by considering energy's carbon intensity at different locations, optimizing client selection, provisioning, and straggler mitigation.

Contribution

EcoLearn introduces a carbon-aware federated learning framework that significantly reduces carbon footprint without sacrificing accuracy or training time.

Findings

Reduces carbon footprint by up to 10.8x.

Maintains model accuracy within 1% of state-of-the-art.

Integrates carbon awareness into multiple FL training aspects.

Abstract

Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy. Since FL model training may span hundreds of devices and is thus resource- and energy-intensive, it has a significant carbon footprint. Importantly, since energy's carbon-intensity differs substantially (by up to 60$\times$) across locations, training on the same device using the same amount of energy, but at different locations, can incur widely different carbon emissions. While prior work has focused on improving FL's resource- and energy-efficiency by optimizing time-to-accuracy, it implicitly assumes all energy has the same carbon intensity and thus does not optimize carbon efficiency, i.e., work done per unit of carbon emitted. To address the problem, we design EcoLearn, which minimizes FL's carbon footprint without significantly affecting model accuracy or training time. EcoLearn achieves a favorable tradeoff by integrating carbon awareness into multiple aspects of FL training, including i) selecting clients with high data utility and low carbon, ii) provisioning more clients during the initial training rounds, and iii) mitigating stragglers by dynamically adjusting client over-provisioning based on carbon. We implement EcoLearn and its carbon-aware FL training policies in the Flower framework and show that it reduces the carbon footprint of training (by up to $10.8$$\times$) while maintaining model accuracy and training time (within $\sim$$1$\%) compared to state-of-the-art approaches.