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

TL;DR

This paper proposes a gradient norm thresholding method to improve client selection in federated learning, enhancing model performance and sustainability by filtering noisy data and balancing carbon budgets.

Contribution

It introduces a novel noise filtering mechanism based on gradient norms to improve client selection in carbon-efficient federated learning.

Findings

Gradient norm thresholding improves model accuracy.

Filtering noisy clients enhances sustainability.

Method balances carbon budgets with model convergence.

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.