Rethinking Personalized Federated Learning with Clustering-Based Dynamic Graph Propagation

Jiaqi Wang, Yuzhong Chen, Yuhang Wu, Mahashweta Das, Hao Yang, Fenglong Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Most existing personalized federated learning approaches are based on intricate designs, which often require complex implementation and tuning. In order to address this limitation, we propose a simple yet effective personalized federated learning framework. Specifically, during each communication round, we group clients into multiple clusters based on their model training status and data distribution on the server side. We then consider each cluster center as a node equipped with model parameters and construct a graph that connects these nodes using weighted edges. Additionally, we update the model parameters at each node by propagating information across the entire graph. Subsequently, we design a precise personalized model distribution strategy to allow clients to obtain the most suitable model from the server side. We conduct experiments on three image benchmark datasets and create synthetic structured datasets with three types of typologies. Experimental results demonstrate the effectiveness of the proposed FedCedar.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings
EditorsDe-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages155-167
Number of pages13
ISBN (Print)9789819722617
DOIs
StatePublished - 2024
Event28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 - Taipei, Taiwan, Province of China
Duration: May 7 2024May 10 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14647 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024
Country/TerritoryTaiwan, Province of China
CityTaipei
Period5/7/245/10/24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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