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CLC number: TP183

On-line Access: 2023-08-29

Received: 2022-06-21

Revision Accepted: 2023-08-29

Crosschecked: 2022-10-27

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Citations:  Bibtex RefMan EndNote GB/T7714


Fengda ZHANG




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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.8 P.1181-1193


Federated unsupervised representation learning

Author(s):  Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   fdzhang@zju.edu.cn, kunkuang@zju.edu.cn

Key Words:  Federated learning, Unsupervised learning, Representation learning, Contrastive learning

Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI. Federated unsupervised representation learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1181-1193.

@article{title="Federated unsupervised representation learning",
author="Fengda ZHANG, Kun KUANG, Long CHEN, Zhaoyang YOU, Tao SHEN, Jun XIAO, Yin ZHANG, Chao WU, Fei WU, Yueting ZHUANG, Xiaolin LI",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Federated unsupervised representation learning
%A Fengda ZHANG
%A Long CHEN
%A Zhaoyang YOU
%A Chao WU
%A Fei WU
%A Yueting ZHUANG
%A Xiaolin LI
%J Frontiers of Information Technology & Electronic Engineering
%V 24
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%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200268

T1 - Federated unsupervised representation learning
A1 - Fengda ZHANG
A1 - Kun KUANG
A1 - Long CHEN
A1 - Zhaoyang YOU
A1 - Tao SHEN
A1 - Jun XIAO
A1 - Yin ZHANG
A1 - Chao WU
A1 - Fei WU
A1 - Yueting ZHUANG
A1 - Xiaolin LI
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 8
SP - 1181
EP - 1193
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2200268

To leverage the enormous amount of unlabeled data on distributed edge devices, we formulate a new problem in federated learning called federated unsupervised representation learning (FURL) to learn a common representation model without supervision while preserving data privacy. FURL poses two new challenges: (1) data distribution shift (non-independent and identically distributed, non-IID) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces; (2) without unified information among the clients in FURL, the representations across clients would be misaligned. To address these challenges, we propose the federated contrastive averaging with dictionary and alignment (FedCA) algorithm. FedCA is composed of two key modules: a dictionary module to aggregate the representations of samples from each client which can be shared with all clients for consistency of representation space and an alignment module to align the representation of each client on a base model trained on public data. We adopt the contrastive approach for local model training. Through extensive experiments with three evaluation protocols in IID and non-IID settings, we demonstrate that FedCA outperforms all baselines with significant margins.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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