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

TL;DR

This paper presents a data-centric approach to reduce the environmental impact of Federated Learning by selecting optimal data subsets and nodes, demonstrated through time series classification with promising results in lowering carbon emissions.

Contribution

It introduces a methodology for data reduction in Federated Learning to minimize ecological footprint, including an interactive system for optimizing FL configurations based on data quality and environmental impact.

Findings

Data reduction decreases training data volume and energy consumption.

Optimal node and data selection reduces carbon emissions.

Methodology improves eco-efficiency in federated time series classification.

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.