Authors - Aneesah Sabar, KA Dilini T Kulawansa Abstract - Federated Learning has emerged as a robust privacy-preserving framework that enables joint model training across multiple distributed clients without sharing raw data. However, the effectiveness of traditional federated learning frameworks is hindered by client heterogeneity, where participants differ in data distribution, computational resources, and communication capabilities. This survey investigates evolution of personalization techniques in Federated Learning that address these challenges by tailoring models to individual clients while maintaining the benefits of global collaboration. The paper categorizes existing personalization approaches into five major groups: local fine-tuning, model interpolation, meta-learning methods, clustered federated learning, and regularization-based techniques. Each method’s core idea, strengths, limitations, and suitability under different heterogeneity conditions are analyzed in detail. The findings indicate that personalization significantly improves fairness, accuracy, and adaptability across heterogeneous clients, though it introduces trade-offs in communication cost, scalability, and privacy. This review concludes that personalization is essential for deploying federated learning in realistic, diverse environments and highlights emerging directions in fairness-aware, resource-efficient, and privacy-preserving personalization. Future research should focus on scalable and dynamic personalization strategies capable of handling evolving client behaviors and large-scale federated systems.
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