Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning

• URL: http://arxiv.org/abs/2310.05397v1
• Date: Mon, 9 Oct 2023 04:23:11 GMT
• Title: Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning
• Authors: Yongxin Guo, Xiaoying Tang, Tao Lin
• Abstract summary: Federated Learning (FL) is an emerging distributed machine learning approach that preserves client privacy by storing data on edge devices. Recent studies have proposed clustering as a solution to tackle client heterogeneity in FL by grouping clients with distribution shifts into different clusters. This paper presents a comprehensive investigation into current clustered FL methods and proposes a four-tier framework to encompass and extend existing approaches.
• Score: 5.045379017315639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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