Survey of personalized federated learning for edge computing scenarios - HE Fan; WANG Yong; YANG Jing; YU Xu (Tongxin xuebao)
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.