Co-MDA: Federated Multisource Domain Adaptation on Black-Box Models

Author:

Liu Xinhui1ORCID,Xi Wei1ORCID,Li Wen2ORCID,Xu Dong3,Bai Gairui1,Zhao Jizhong1

Affiliation:

1. Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China

2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Department of Computer Science, The University of Hong Kong, Hong Kong, China

Funder

National Key Research and Development Program of China

The Hong Kong Jockey Club Charities Trust

National Natural Science Foundation of China

Startup Funding and the Seed Funding for Basic Research for New Staff from The University of Hong Kong

UBTECH Robotics

Sichuan Science and Technology Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A comprehensive survey of federated transfer learning: challenges, methods and applications;Frontiers of Computer Science;2024-07-23

2. Multi-source-free Domain Adaptive Object Detection;International Journal of Computer Vision;2024-07-11

3. Lighter, Better, Faster Multi-source Domain Adaptation with Gaussian Mixture Models and Optimal Transport;Lecture Notes in Computer Science;2024

4. Personalized Federated Semi-Supervised Learning with Black-Box Models;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

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