[2603.04194] Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding

Computer Science > Machine Learning arXiv:2603.04194 (cs) [Submitted on 4 Mar 2026] Title:Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding Authors:Patrick Wilhelm, Inese Yilmaz, Odej Kao View a PDF of the paper titled Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding, by Patrick Wilhelm and 2 other authors View PDF HTML (experimental) Abstract:Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data centers, leveraging renewable energy sources to reduce the carbon footprint of AI training. Various client selection strategies have been developed to align the volatility of renewable energy with stable and fair model training in a federated system. However, due to the privacy-preserving nature of Federated Learning, the quality of data on client devices remains unknown, posing challenges for effective model training. In this paper, we introduce a modular approach on top to state-of-the-art client selection strategies for carbon-efficient Federated Learning. Our method enhances robustness by incorporating a noisy client data filtering, improving both model performance and sustainability in scenarios with unknown data quality. Additionally, we explore the impact of carbon budgets on model convergence, balancing efficiency and sustainability. Through extensi...