Federated Learning (FL) is an emerging distributed machine learning approach
that preserves client privacy by storing data on edge devices. However, data
heterogeneity among clients presents challenges in training models that perform
well on all local distributions. Recent studies have proposed clustering as a
solution to tackle client heterogeneity in FL by grouping clients with
distribution shifts into different clusters. However, the diverse learning
frameworks used in current clustered FL methods make it challenging to
integrate various clustered FL methods, gather their benefits, and make further
improvements.
To this end, this paper presents a comprehensive investigation into current
clustered FL methods and proposes a four-tier framework, namely HCFL, to
encompass and extend existing approaches. Based on the HCFL, we identify the
remaining challenges associated with current clustering methods in each tier
and propose an enhanced clustering method called HCFL+ to address these
challenges. Through extensive numerical evaluations, we showcase the
effectiveness of our clustering framework and the improved components. Our code
will be publicly available.

通过综合研究当前对聚类型联邦学习方法，本文提出了一个四层框架（HCFL），并针对每一层中当前聚类方法面临的挑战提出了一个增强聚类方法 HCFL+，通过广泛的数值评估展示了我们的聚类框架和改进组件的有效性。