IoTSL: Toward Efficient Distributed Learning for Resource-Constrained Internet of Things-Reference-Cited by-同舟云学术

IoTSL: Toward Efficient Distributed Learning for Resource-Constrained Internet of Things

Published:2023-06-01 Issue:11 Volume:10 Page:9892-9905
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Feng Xingyu¹^ORCID,Luo Chengwen¹^ORCID,Chen Jiongzhang¹,Huang Yijing¹,Zhang Jin¹^ORCID,Xu Weitao²^ORCID,Li Jianqiang¹^ORCID,Leung Victor C. M.¹^ORCID

Affiliation:

1. National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

2. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

Funder

National Natural Science Foundation of China

Stable Support Plan for Higher Education Institutions in Shenzhe

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10128761/10013364.pdf?arnumber=10013364

Reference46 articles.

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2. Aggregation service for federated learning: An efficient, secure, and more resilient realization;zheng;IEEE Trans Dependable Secure Comput,2022

3. Federated Learning: Challenges, Methods, and Future Directions

4. Secure and Efficient Federated Learning for Smart Grid With Edge-Cloud Collaboration

5. Time-Constrained Ensemble Sensing With Heterogeneous IoT Devices in Intelligent Transportation Systems

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