Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV-Reference-Cited by-同舟云学术

Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV

Published:2024-06-01 Issue:11 Volume:11 Page:19721-19735
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Sun Kangkang¹^ORCID,Xu Hansong¹^ORCID,Hua Kun²,Lin Xi¹^ORCID,Li Gaolei¹^ORCID,Jiang Tigang³^ORCID,Li Jianhua¹^ORCID

Affiliation:

1. Shanghai Key Laboratory of Integrated Administration Technologies for Information Security, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Department of Electrical Engineering, California Polytechnic State University, San Luis Obispo, CA, USA

3. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

Funder

National Natural Science Foundation of China

Action Plan of the Science and Technology Innovation of the Science and Technology Commission of Shanghai Municipality

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10536937/10478294.pdf?arnumber=10478294

Reference49 articles.

1. Privacy-Preserved Framework for Short-Term Probabilistic Net Energy Forecasting

2. 5G Vehicle-to-Everything Services: Gearing Up for Security and Privacy

3. Privacy-Preserving Graph Matching Query Supporting Quick Subgraph Extraction

4. Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

5. Efficient Rate-Splitting Multiple Access for the Internet of Vehicles: Federated Edge Learning and Latency Minimization