Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning

2026-02-24

AI Summary

This work addresses the performance degradation of global models in federated learning caused by client data heterogeneity. To mitigate this issue, the authors propose Terraform, a novel approach that uniquely integrates gradient information with a deterministic client selection mechanism. By leveraging gradient updates to characterize client heterogeneity, Terraform enables precise participant selection, thereby enhancing both model accuracy and training efficiency. The method introduces a gradient-based heterogeneity metric coupled with a deterministic selection algorithm, yielding significant performance gains within the standard federated learning framework. Experimental results demonstrate that Terraform improves accuracy by up to 47% compared to existing methods, while ablation studies and training-time analyses further confirm its efficiency and robustness.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model's accuracy. However, these updates neither accurately represent a client's heterogeneity nor are their selection methods deterministic. We mitigate these limitations by introducing Terraform, a novel client selection methodology that uses gradient updates and a deterministic selection algorithm to select heterogeneous clients for retraining. This bi-pronged approach allows Terraform to achieve up to 47 percent higher accuracy over prior works. We further demonstrate its efficiency through comprehensive ablation studies and training time analyses, providing strong justification for the robustness of Terraform.

Problem

• Federated Learning
• Statistical Heterogeneity
• Client Selection
• Model Accuracy
• Distributed Learning

Innovation

• heterogeneity-aware
• client selection
• federated learning
• gradient-based selection
• deterministic algorithm