GreenDFL: a Framework for Assessing the Sustainability of Decentralized Federated Learning Systems

TL;DR

GreenDFL is a comprehensive framework that assesses and enhances the environmental sustainability of decentralized federated learning systems by analyzing factors affecting energy use and emissions, and proposing optimization algorithms.

Contribution

This work introduces GreenDFL, a novel, fully implementable framework for evaluating and improving the sustainability of DFL systems, including new algorithms for energy efficiency and carbon reduction.

Findings

GreenDFL effectively quantifies energy consumption and carbon emissions in DFL.

Optimization algorithms reduce energy use and emissions during DFL training.

Empirical results demonstrate improved sustainability in real-world DFL deployments.

Abstract

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data and model aggregation, enhancing privacy and resilience. However, its sustainability remains underexplored, as energy consumption and carbon emissions vary across different system configurations. Understanding the environmental impact of DFL is crucial for optimizing its design and deployment. This work aims to develop a comprehensive and operational framework for assessing the sustainability of DFL systems. To address it, this work provides a systematic method for quantifying energy consumption and carbon emissions, offering insights into improving the sustainability of DFL. This work proposes GreenDFL, a fully implementable framework that has been integrated into a real-world DFL platform. GreenDFL systematically analyzes the impact of various factors, including hardware accelerators, model architecture, communication medium, data distribution, network topology, and federation size, on the sustainability of DFL systems. Besides, a sustainability-aware aggregation algorithm (GreenDFL-SA) and a node selection algorithm (GreenDFL-SN) are developed to optimize energy efficiency and reduce carbon emissions in DFL training. Empirical experiments are conducted on multiple datasets, measuring energy consumption and carbon emissions at different phases of the DFL lifecycle. The proposed GreenDFL provides a comprehensive and practical approach for assessing the sustainability of DFL systems. Furthermore, it offers best practices for improving environmental efficiency in DFL, making sustainability considerations more actionable in real-world deployments.