Federated Multimodal Learning for Privacy-Preserving Driver Break Recommendations in Consumer Electronics-Reference-Cited by-同舟云学术

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Federated Multimodal Learning for Privacy-Preserving Driver Break Recommendations in Consumer Electronics

Published:2023 Issue: Volume: Page:1-1
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Liu Peng¹^ORCID,Jiang Longfei²,Lin Huaming³,Hu Jia⁴^ORCID,Garg Sahil⁵^ORCID,Alrashoud Mubarak⁶^ORCID

Affiliation:

1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

2. HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, China

3. Hangzhou Security and Technology Evaluation Center, Hangzhou, China

4. University of Exeter, UK

5. Electrical Engineering Department, École de technologie supérieure, Université du Québec, Montréal, Canada

6. Department of Software Engineering (SWE), College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/30/8306365/10342815.pdf?arnumber=10342815

Cited by 2 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

1. Next-Generation Consumer Electronics Data Auditing Scheme Toward Cloud–Edge Distributed and Resilient Machine Learning;IEEE Transactions on Consumer Electronics;2024-02

2. MIGAN: A Privacy Leakage Evaluation Scheme for CIoT-Based Federated Learning Users;IEEE Transactions on Consumer Electronics;2024-02

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