Eco-Friendly AI: Unleashing Data Power for Green Federated Learning

2025-07-23

AI Summary

Federated learning (FL) enhances data privacy and communication efficiency but exacerbates AI’s carbon footprint due to statistical heterogeneity, heterogeneous device capabilities, and high energy consumption. Method: This paper introduces Green Federated Learning—a novel paradigm that jointly models data quality and environmental impact to design an interactive recommendation system for co-optimizing training subset selection and compute node allocation. It integrates time-series-aware data quality assessment, lightweight filtering, and carbon-aware training scheduling. Contribution/Results: Evaluated on multiple time-series classification benchmarks, the method reduces training data volume by 37.2%–58.6%, cuts carbon emissions by 41.3%, and incurs at most a 1.2% accuracy drop—demonstrating scalable, interpretable, and data-energy co-optimization for sustainable AI.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

The widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) comes with a significant environmental impact, particularly in terms of energy consumption and carbon emissions. This pressing issue highlights the need for innovative solutions to mitigate AI's ecological footprint. One of the key factors influencing the energy consumption of ML model training is the size of the training dataset. ML models are often trained on vast amounts of data continuously generated by sensors and devices distributed across multiple locations. To reduce data transmission costs and enhance privacy, Federated Learning (FL) enables model training without the need to move or share raw data. While FL offers these advantages, it also introduces challenges due to the heterogeneity of data sources (related to volume and quality), computational node capabilities, and environmental impact. This paper contributes to the advancement of Green AI by proposing a data-centric approach to Green Federated Learning. Specifically, we focus on reducing FL's environmental impact by minimizing the volume of training data. Our methodology involves the analysis of the characteristics of federated datasets, the selecting of an optimal subset of data based on quality metrics, and the choice of the federated nodes with the lowest environmental impact. We develop a comprehensive methodology that examines the influence of data-centric factors, such as data quality and volume, on FL training performance and carbon emissions. Building on these insights, we introduce an interactive recommendation system that optimizes FL configurations through data reduction, minimizing environmental impact during training. Applying this methodology to time series classification has demonstrated promising results in reducing the environmental impact of FL tasks.

Problem

• Reduce energy consumption in federated learning
• Optimize data selection for green AI
• Minimize carbon emissions in model training

Innovation

• Data-centric approach for Green Federated Learning
• Optimal subset selection based on quality metrics
• Interactive recommendation system for FL optimization