A Fairness Perspective on Client Selection and Aggregation Methods for Non-IID Mitigation in Federated Learning: A Survey

Abstract

Federated learning (FL) is a promising approach for training distributed machine learning models while preserving clients’ data privacy. However, in real-world FL systems, data are often not independent and identically distributed (non-IID). This heterogeneity can slow convergence, degrade model performance, and increase client drift. To address these challenges, numerous methods have been proposed to mitigate non-IID data effects by optimizing client selection, local training, and model aggregation strategies. Despite their effectiveness in improving performance and efficiency, these methods rarely consider fairness across clients. Improving global accuracy does not guarantee balanced participation, influence, or outcomes, which may lead to biased model behavior across clients. In this survey, we review existing non-IID mitigation methods in FL from a fairness perspective and provide a systematic analysis of their implicit impact on client participation and influence. Unlike prior surveys that treat fairness as a separate research direction, this work analyzes how these methods designed for non-IID mitigation implicitly shape fairness outcomes across clients. Our taxonomy classifies existing methods into three categories—fairness-aware, semi-fairness-aware, and fairness-unaware—based on their design strategies for client selection and model aggregation. Using this taxonomy, we analyze the advantages, trade-offs, and limitations of each category and highlight that mitigating non-IID data does not guarantee fairness across clients. Finally, we identify open challenges and outline future directions, including system-level FL design that jointly considers non-IID mitigation and fairness and the development of standardized fairness evaluation metrics. Overall, this survey aims to provide a structured perspective on the relationship between non-IID mitigation and fairness and support the development of more balanced and scalable FL systems under non-IID conditions.