Personalized Federated Learning via Heterogeneous Modular Networks

Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang

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

4 Scopus citations

Abstract

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite its popularity, it has been observed that existing PFL approaches result in sub-optimal solutions when the joint distribution among local clients diverges. To address this issue, we present Federated Modular Network (FedMN), a novel PFL approach that adaptively selects sub-modules from a module pool to assemble heterogeneous neural architectures for different clients. FedMN adopts a light-weighted routing hypernetwork to model the joint distribution on each client and produce the personalized selection of the module blocks for each client. To reduce the communication burden in existing FL, we develop an efficient way to interact between the clients and the server. We conduct extensive experiments on the real-world test beds and the results show both effectiveness and efficiency of the proposed FedMN over the baselines.

Original languageEnglish (US)
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
EditorsXingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1197-1202
Number of pages6
ISBN (Electronic)9781665450997
DOIs
StatePublished - 2022
Event22nd IEEE International Conference on Data Mining, ICDM 2022 - Orlando, United States
Duration: Nov 28 2022Dec 1 2022

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2022-November
ISSN (Print)1550-4786

Conference

Conference22nd IEEE International Conference on Data Mining, ICDM 2022
Country/TerritoryUnited States
CityOrlando
Period11/28/2212/1/22

All Science Journal Classification (ASJC) codes

  • General Engineering

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