Vertical Federated Learning: Concepts, Advances, and Challenges-Reference-Cited by-同舟云学术

Vertical Federated Learning: Concepts, Advances, and Challenges

Published:2024-07 Issue:7 Volume:36 Page:3615-3634
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Liu Yang¹^ORCID,Kang Yan²^ORCID,Zou Tianyuan¹^ORCID,Pu Yanhong¹^ORCID,He Yuanqin²^ORCID,Ye Xiaozhou³^ORCID,Ouyang Ye³^ORCID,Zhang Ya-Qin¹^ORCID,Yang Qiang²^ORCID

Affiliation:

1. Institute for AI Industry Research, Tsinghua University, Beijing, China

2. Webank, Shenzhen, China

3. AsiaInfo Technologies, Beijing, China

Funder

National Key R&D Program of China

Tsinghua-Toyota Joint Research Institute inter-disciplinary Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/69/10549876/10415268.pdf?arnumber=10415268

Reference193 articles.

1. Federated Learning

2. Communication-efficient learning of deep networks from decentralized data;McMahan

3. Federated Machine Learning

4. Federated learning for privacy-preserving AI

5. Federated Transfer Learning for EEG Signal Classification

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