【摘要】 针对传统联邦学习在边缘计算场景中面临边缘节点数据异质、个性化需求等挑战，导致全局模型难以适配多样化的边缘节点，对面向边缘计算场景的个性化联邦学习研究进展进行系统性的综述。首先，回顾个性化联邦学习的背景及意义并分析数据异质性对联邦学习的影响。其次，介绍数据异质性的概念，并归纳数据异质性的常见形式。然后，梳理现阶段面向边缘计算场景的个性化联邦学习研究方法，主要包括基于数据、基于客户端模型优化、基于服务器聚合优化、基于全局架构优化、基于大模型以及基于原型学习的5种关键方法。最后，对其发展趋势进行探讨并展望未来可能的研究方向，为未来个性化联邦学习领域的研究提供指引和方向。更多还原

【Abstract】 In view of the challenges faced by traditional federated learning in edge computing scenarios, such as data heterogeneity and personalized requirements among edge nodes, limiting the adaptability of the global model. So, a comprehensive review of recent advances in personalized federated learning for edge computing scenarios was provided. Firstly, the background and scientific significance of personalized federated learning were elaborated, followed by rigorous analysis of data heterogeneity’s impacts. Secondly, data heterogeneity was formally defined and classified. Subsequently, existing approaches were categorized into five key methodologies: data-based methods, client-side model optimization, server-side aggregation optimization, global architecture optimization, large model, and prototype-based learning methods. Finally, to guide ongoing developments in the field, future trends and outlines potential research directions were explored.更多还原