A comprehensive survey on client selection strategies in federated learning

Abstract

Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy. Client selection plays a crucial role in determining the overall performance and efficiency of the FL training process. However, the heterogeneity of clients in terms of computational resources, data quality, and network conditions poses significant challenges in selecting the optimal set of clients for each training round. This survey provides a comprehensive review of client selection methodologies in FL, addressing the challenges and opportunities in this field. We present a systematic categorization of existing client selection techniques based on their underlying principles and objectives, and discuss the key challenges, including resource constraints, data heterogeneity, and security concerns. Additionally, we provide a comparative analysis of the surveyed techniques, highlighting their strengths, limitations, and suitability for various FL scenarios. Furthermore, we identify open research problems and propose future research directions, emphasizing the need for more efficient, adaptive, and secure selection strategies. This survey serves as a comprehensive framework for understanding client selection in FL, bridging gaps in the existing literature and providing guidance for future research, including ethical considerations and domain-specific applications. Our work aims to support practitioners in making informed decisions and to stimulate further research in this critical area of FL.