A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic Encryption-Reference-Cited by-同舟云学术

A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic Encryption

Author:

Gong Maoguo¹^ORCID,Zhang Yuanqiao¹^ORCID,Gao Yuan¹^ORCID,Qin A. K.²^ORCID,Wu Yue¹^ORCID,Wang Shanfeng¹,Zhang Yihong¹

Affiliation:

1. Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an, China

2. Department of Computing Technologies, Swinburne University of Technology, Melbourne, VIC, Australia

Funder

National Natural Science Foundation of China

Postdoctoral Science Foundation of China

Fundamental Research Funds for the Central Universities

Australian Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference46 articles.

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

3. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption;Hardy;arXiv:1711.10677,2017

4. Parallel distributed logistic regression for vertical federated learning without third-party coordinator;Yang;arXiv:1911.09824,2019

5. VAFL: A method of vertical asynchronous federated learning;Chen;arXiv:2007.06081,2020

Cited by 7 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