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This review analyzes federated learning (FL) challenges posed by data heterogeneity.

May 6, 2026Expert Systems0 citations

A Review of Federated Learning Under Data Heterogeneity

WYWentao YueLanzhou University TLTianyou LaiLanzhou University QMQingyu MaoShenzhen University

Puntos clave

This review analyzes federated learning (FL) challenges posed by data heterogeneity.
Reviewed areas of data heterogeneity in federated learning
Examined algorithmic advances like personalized FL and transfer learning
Synthesized applications across sectors such as healthcare and finance
Identified challenges and suggested directions for future research
Data heterogeneity impairs convergence and model performance
Challenges such as fairness and scalability are critical in FL deployment
Proposed solutions indicate potential for fairness and robustness in various fields including healthcare and IoT

Resumen

ABSTRACT Federated learning (FL) has emerged as an impactful paradigm for privacy‐preserving machine learning, and allows model training without the need to share raw data. However, data heterogeneity across clients challenges practical FL deployment. Data space heterogeneity and statistical heterogeneity create significant training difficulties. System heterogeneity imposes additional external constraints. These combined factors impair convergence and reduce model performance. They also raise concerns regarding fairness, scalability and robustness. Focused on data heterogeneity, this review provides a structured analysis of FL. It encompasses three key areas: core categorizations of data heterogeneity, algorithmic advances (e.g., personalized FL, mixture‐of‐experts architectures, transfer learning‐based solutions) and system‐level techniques spanning communication optimization, resource adaptation and secure collaboration. We further synthesize benchmark efforts and real‐world applications in healthcare, finance, nuclear power and the Internet of Things (IoT)/edge computing to highlight the practical implications of heterogeneity‐aware FL. Finally, we identify key challenges and outline promising research directions towards scalable, fair and adaptive FL systems capable of operating in complex real‐world settings. This survey aims to serve as a reference point and conceptual roadmap for future research in heterogeneous FL.

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