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

2026-03-04

AI Summary

This work addresses the performance degradation of existing loss-based client selection strategies in carbon-efficient federated learning, which arises from data noise on client devices and adversely impacts model convergence and sustainability. To mitigate this issue, the paper proposes a noise-aware client selection mechanism that leverages a probing round to compute gradient norms and applies a threshold to filter out low-quality clients. This approach is the first to integrate data quality robustness into a federated learning framework under carbon constraints. By jointly modeling renewable energy intermittency and enforcing carbon budget limits, the method effectively avoids selecting noisy clients while significantly improving model accuracy and training stability, thereby achieving a balance between computational efficiency and environmental sustainability.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 extensive evaluations, we demonstrate that modern client selection strategies based on local client loss tend to select clients with noisy data, ultimately degrading model performance. To address this, we propose a gradient norm thresholding mechanism using probing rounds for more effective client selection and noise detection, contributing to the practical deployment of carbon-efficient Federated Learning.

Problem

• Federated Learning
• Client Selection
• Noise-aware
• Carbon Efficiency
• Data Quality

Innovation

• gradient norm thresholding
• noise-aware client selection
• carbon-efficient federated learning
• probing rounds
• data quality filtering