ORCID
- Steven Davy: 0000-0002-3300-1152
Abstract
Federated Learning (FL) offers a decentralized approach to training large language models (LLMs), addressing critical concerns around data privacy and transmission costs. However, FL's inherent distributed training paradigm can lead to increased energy consumption and carbon emissions, especially with random or uninformed client selection. To tackle this issue, we propose SustainFed-LLM, a novel Q-learning-based client selection framework that integrates real-time renewable energy availability, carbon intensity data, and fairness considerations. By dynamically assessing client performance, spare capacity, and sustainability metrics, SustainFed-LLM optimizes client participation to minimize environmental impact while maintaining model accuracy. SustainFed-LLM significantly reduces energy consumption by up to 50%, while achieving convergence 30-70% faster compared to conventional selection strategies. We also analyze the communication overhead and computation cost, finding 30% fewer transmitted bytes and a 14% drop in FLOPS. The proposed framework also promotes fairer client participation, as evidenced by a reduced Gini coefficient. These findings underscore the potential of SustainFed-LLM to advance green AI, providing an effective pathway for large-scale sustainable and energy-efficient LLM training.
DOI Link
Publication Date
2025-10-21
Event
28th European Conference on Artificial Intelligence, ECAI 2025, including 14th Conference on Prestigious Applications of Intelligent Systems, PAIS 2025
Publication Title
ECAI 2025 - 28th European Conference on Artificial Intelligence, including 14th Conference on Prestigious Applications of Intelligent Systems, PAIS 2025 - Proceedings
Publisher
IOS Press BV
ISBN
9781643686318
ISSN
0922-6389
First Page
4537
Last Page
4544
Deposit Date
2026-03-03
Funding
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 21/FFP-A/9174.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Additional Links
Recommended Citation
Iftikhar, Sunbal; Khan, Hassan; and Davy, Steven, "SustainFed-LLM: Renewable Energy Aware Client Selection for Energy Efficient Federated Training of Large Language Models" (2025). Research Outputs: 2025-Present. 2.
https://arrow.tudublin.ie/csdtro/2