GreenFL: Carbon-efficient Federated Learning over RE Powered Edge Computing Systems
Hanlong Liao, Yu Zhang, Lailong Luo, Deke Guo, Guoming Tang · 2025
The prominent paradigm of federated learning (FL) is increasingly being applied to emerging and cross-silo applications, particularly with edge computing systems serving as pivotal agents. However, this shift also renders FL training more energy and carbon intensive. To this end, we propose GreenFL, a carbon-aware FL training framework designed to systematically navigate the trade-offs between carbon emission, training accuracy, and training efficiency. GreenFL employs a hybrid training strategy that combines inter-group asynchronous training and intra-group synchronous training to mitigate the straggler effect caused by inefficient participants. In the overall design of the framework, we promote the participation of edge computing nodes with abundant renewable energy sources and implement strategic participant selection to balance carbon emissions and training accuracy. We prove the solvability of optimizing the selection strategy and provide an online greed-based solution based on penalty values and bipartite greedy algorithms. Through extensive data-driven experiments, we demonstrate that GreenFL can significantly improve the carbon efficiency of the entire FL procedure, while maintaining or exceeding state-of-the-art levels of training accuracy and efficiency.