Toward Robust Hierarchical Federated Learning in Internet of Vehicles

Author:

Zhou Hongliang1,Zheng Yifeng1ORCID,Huang Hejiao1ORCID,Shu Jiangang2ORCID,Jia Xiaohua1ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

2. Department of New Networks, Peng Cheng Laboratory, Shenzhen, China

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Shenzhen Science and Technology Program

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Reference51 articles.

1. How to backdoor federated learning;bagdasaryan;Proc AISTATS,2020

2. Local model poisoning attacks to Byzantine-robust federated learning;fang;Proc USENIX Security07,2020

3. Privacy-Preserving Aggregation for Federated Learning-Based Navigation in Vehicular Fog

4. The limitations of federated learning in sybil settings;fung;Proc RAID,2020

5. Label sanitization against label flipping poisoning attacks;paudice;Proc Eur Conf Mach Learn Knowl Discovery Databases,2018

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