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

2025-02-27

AI Summary

This work addresses the lack of quantitative sustainability assessment in decentralized federated learning (DFL) systems. We propose GreenDFL, the first holistic framework for lifecycle energy consumption and carbon emission evaluation—spanning hardware, model architecture, communication protocols, network topology, and data distribution. Methodologically, GreenDFL introduces a carbon-aware dynamic node selection algorithm and a lightweight aggregation mechanism to reduce carbon footprint without compromising model accuracy; it further incorporates heterogeneous hardware efficiency modeling, sustainability-driven model compression, and carbon-aware early stopping. Experimental results reveal that local training dominates DFL’s carbon emissions; substituting GPUs for CPUs, simplifying models, deploying in low-carbon regions, and applying early stopping collectively reduce emissions by 37–62%. GreenDFL enables cross-configuration, reproducible, and comparable sustainability evaluation, establishing a foundation for environmentally responsible DFL deployment.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Sustainability and climate impact of web technologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Decentralized Federated Learning (DFL) is an emerging paradigm that enables collaborative model training without centralized data 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 study aims to assess the sustainability of DFL systems by analyzing factors that influence energy consumption and carbon emissions. Additionally, it proposes sustainability-aware optimization strategies, including a node selection algorithm and an aggregation method, to reduce the environmental footprint of DFL without compromising model performance. The proposed framework, named GreenDFL, systematically evaluates the impact of hardware accelerators, model architecture, communication medium, data distribution, network topology, and federation size on sustainability. Empirical experiments are conducted on multiple datasets using different system configurations, measuring energy consumption and carbon emissions across various phases of the DFL lifecycle. Results indicate that local training dominates energy consumption and carbon emissions, while communication has a relatively minor impact. Optimizing model complexity, using GPUs instead of CPUs, and strategically selecting participating nodes significantly improve sustainability. Additionally, deploying nodes in regions with lower carbon intensity and integrating early stopping mechanisms further reduce emissions. The proposed framework provides a comprehensive and practical computational 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.

Problem

• Assess sustainability of Decentralized Federated Learning systems
• Analyze energy consumption and carbon emissions factors
• Propose optimization strategies to reduce environmental impact

Innovation

• Sustainability-aware optimization strategies
• GreenDFL framework for DFL
• Energy-efficient node selection algorithm