A Granularity-Centered Taxonomy of Personalized Federated Learning

Authors

• Ei Ei Nyein Chan, The University of Texas Rio Grande Valley
• Sergei Chuprov, The University of Texas Rio Grande ValleyFollow
• Pretom Roy Ovi, University of North Texas
• Kamrul Hasan, Tennessee State University

Document Type

Article

Publication Date

8-5-2026

Abstract

Personalized Federated Learning (PFL) has emerged as a key approach to address performance degradation in FL systems under heterogeneous client data. While existing surveys typically categorize PFL methods based on optimization strategies or system-level mechanisms, they often overlook a fundamental question: where is personalization embedded within the model architecture? In this survey, we bridge this knowledge gap and introduce a granularity-centered taxonomy that organizes PFL approaches according to the structural depth of personalization, ranging from head-layer and layer-wise adaptation to model-wise and parameter-wise customization. This novel perspective helps practitioners select appropriate personalization strategies based on model architecture, data heterogeneity, and system constraints. By analyzing representative works published between 2016 and 2026, we identify recurring design bottlenecks and highlight key opportunities for improving personalization across different granularity levels. While existing surveys focus solely on algorithms, our taxonomy provides practitioners and researchers with the informed guidance needed to design scalable and effective PFL systems. Building upon this analysis, we also formulate and discuss several unaddressed open challenges currently present in the field, highlighting specific roadblocks and unresolved questions that must be addressed to drive the future evolution of PFL methodologies.

Comments

© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.    

Recommended Citation

Nyein Chan, Ei Ei, Sergei Chuprov, Pretom Roy Ovi, and Kamrul Hasan. "A Granularity-Centered Taxonomy of Personalized Federated Learning." Electronics 15, no. 15 (2026): 3481.  https://doi.org/10.3390/electronics15153481

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Electronics

DOI

https://doi.org/10.3390/electronics15153481