联邦无监督表示学习

张凤达; 况琨; 陈隆; 游兆阳; 沈弢; 肖俊; 张寅; 吴超; 吴飞; 庄越挺; 李晓林

doi:10.1631/FITEE.2200268

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联邦无监督表示学习

常规文章 | Updated：2023-08-29

- 联邦无监督表示学习
- Federated unsupervised representation learning
- 信息与电子工程前沿（英文） 2023年24卷第8期页码：1181-1193
- Affiliations：
  
  1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  2.School of Public Affairs, Zhejiang University, Hangzhou 310027, China
  3.Tongdun Technology, Hangzhou 310000, China
  4.Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou 310018, China
  5.ElasticMind.AI Technology Inc., Hangzhou 310018
- Author bio：
  
  †E-mail: fdzhang@zju.edu.cn
  ‡Corresponding author
- Funds：
  
  National Key Research & Development Project of China(2021ZD0110700;2021ZD0110400);National Natural Science Foundation of China(U20A20387;U19B2043;61976185;U19B2042);Zhejiang Natural Science Foundation, China(LR19F020002);Zhejiang Innovation Foundation, China(2019R52002);Fundamental Research Funds for the Central Universities, China
- DOI：10.1631/FITEE.2200268
  中图分类号： TP183
- 收稿：2022-06-21，
  
  录用：2022-10-27，
  
  纸质出版：2023-08-0
- Accepted：
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张凤达, 况琨, 陈隆, 等. 联邦无监督表示学习[J]. 信息与电子工程前沿（英文）, 2023,24(8):1181-1193.

Fengda ZHANG, Kun KUANG, Long CHEN, et al. Federated unsupervised representation learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1181-1193.
张凤达, 况琨, 陈隆, 等. 联邦无监督表示学习[J]. 信息与电子工程前沿（英文）, 2023,24(8):1181-1193. DOI： 10.1631/FITEE.2200268.

Fengda ZHANG, Kun KUANG, Long CHEN, et al. Federated unsupervised representation learning[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(8): 1181-1193. DOI： 10.1631/FITEE.2200268.

摘要

为利用分布式边缘设备上大量未标记数据，我们在联邦学习中提出一个称为联邦无监督表示学习（FURL）的新问题，以在没有监督的情况下学习通用表示模型，同时保护数据隐私。FURL提出了两个新挑战：（1）客户端之间的数据分布转移（非独立同分布）会使本地模型专注于不同的类别，从而导致表示空间的不一致；（2）如果FURL中客户端之间没有统一的信息，客户端之间的表示就会错位。为了应对这些挑战，我们提出带字典和对齐的联合对比平均（FedCA）算法。FedCA由两个关键模块组成：字典模块，用于聚合来自每个客户端的样本表示并与所有客户端共享，以实现表示空间的一致性；对齐模块，用于将每个客户端的表示与基于公共数据训练的基础模型对齐。我们采用对比方法进行局部模型训练，通过在3个数据集上独立同分布和非独立同分布设定下的大量实验，我们证明FedCA以显著的优势优于所有基线方法。

Abstract

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.

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