SAM: An Efficient Approach With Selective Aggregation of Models in Federated Learning-Reference-Cited by-同舟云学术

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SAM: An Efficient Approach With Selective Aggregation of Models in Federated Learning

Published:2024-06-01 Issue:11 Volume:11 Page:20769-20783
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Shi Yuchen¹^ORCID,Fan Pingyi¹^ORCID,Zhu Zheqi¹^ORCID,Peng Chenghui²,Wang Fei²,Letaief Khaled B.³^ORCID

Affiliation:

1. Department of Electronic Engineering, Tsinghua University, Beijing, China

2. Wireless Technology Laboratory, Huawei Technologies, Shanghai, China

3. Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Pokfulam, Hong Kong

Funder

National Key Research and Development Program of China

Hong Kong Research Grants Council through the Areas of Excellence Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10536937/10461028.pdf?arnumber=10461028

Reference42 articles.

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

2. Advances and Open Problems in Federated Learning

3. The Roadmap to 6G: AI Empowered Wireless Networks

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

5. Convergence Analysis and System Design for Federated Learning Over Wireless Networks

Cited by 3 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. Analysis of Federated Learning Paradigm in Medical Domain: Taking COVID-19 as an Application Use Case;Applied Sciences;2024-05-11

2. A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications;Computer Modeling in Engineering & Sciences;2024

3. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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