Adaptive Vertical Federated Learning on Unbalanced Features-Reference-Cited by-同舟云学术

Adaptive Vertical Federated Learning on Unbalanced Features

Published:2022-12-01 Issue:12 Volume:33 Page:4006-4018
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Zhang Jie¹^ORCID,Guo Song¹^ORCID,Qu Zhihao²^ORCID,Zeng Deze³^ORCID,Wang Haozhao⁴^ORCID,Liu Qifeng⁵,Zomaya Albert Y.⁶^ORCID

Affiliation:

1. Department of Computing, The Hong Kong Polytechnic University, Hong Kong

2. Key Laboratory of Water Resources Big Data Technology of Ministry of Water Resources, School of Computer and Information, Hohai University, Nanjing, China

3. School of Computer Science and Technology, China University of Geosciences, Wuhan, China

4. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

5. Department of Computer Science, Hong Kong Baptist University, Hong Kong

6. High Performance Computing & Networking, School of Information Technologies, Sydney University, Camperdown, NSW, Australia

Funder

Key-Area Research and Development Program of Guangdong Province

Hong Kong RGC Research Impact Fund

General Research Fund

National Natural Science Foundation of China

Shenzhen Science and Technology Innovation Commission

Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/71/9790018/09783035.pdf?arnumber=9783035

Reference52 articles.

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4. Distributed Ridge Regression with Feature Partitioning

5. Federated Machine Learning

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