Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT

Jiaqi Wang, Shenglai Zeng, Zewei Long, Yaqing Wang, Houping Xiao, Fenglong Ma

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

8 Scopus citations

Abstract

Federated learning (FL) enables multiple clients to train models collaboratively without sharing local data, which has achieved promising results in different areas, including the Internet of Things (IoT). However, end IoT devices do not have abilities to automatically annotate their collected data, which leads to the label shortage issue at the client side. To collaboratively train an FL model, we can only use a small number of labeled data stored on the server. This is a new yet practical scenario in federated learning, i.e., labels-at-server semi-supervised federated learning (SemiFL). Although several SemiFL approaches have been proposed recently, none of them can focus on the personalization issue in their model design. IoT environments make SemiFL more challenging, as we need to take device computational constraints and communication cost into consideration simultaneously. To tackle these new challenges together, we propose a novel SemiFL framework named pFedKnow. pFedKnow generates lightweight personalized client models via neural network pruning techniques to reduce communication cost. Moreover, it incorporates pretrained large models as prior knowledge to guide the aggregation of personalized client models and further enhance the framework performance. Experiment results on both image and text datasets show that the proposed pFedKnow outperforms state-of-the-art baselines as well as reducing considerable communication cost. The source code of the proposed pFedKnow is available at https://github.com/JackqqWang/pfedknow/tree/master.

Original languageEnglish (US)
Title of host publication2023 SIAM International Conference on Data Mining, SDM 2023
PublisherSociety for Industrial and Applied Mathematics Publications
Pages496-504
Number of pages9
ISBN (Electronic)9781611977653
StatePublished - 2023
Event2023 SIAM International Conference on Data Mining, SDM 2023 - Minneapolis, United States
Duration: Apr 27 2023Apr 29 2023

Publication series

Name2023 SIAM International Conference on Data Mining, SDM 2023

Conference

Conference2023 SIAM International Conference on Data Mining, SDM 2023
Country/TerritoryUnited States
CityMinneapolis
Period4/27/234/29/23

All Science Journal Classification (ASJC) codes

  • Education
  • Information Systems

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