ISFL: Federated Learning for Non-i.i.d. Data With Local Importance Sampling-Reference-Cited by-同舟云学术

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ISFL: Federated Learning for Non-i.i.d. Data With Local Importance Sampling

Published:2024-08-15 Issue:16 Volume:11 Page:27448-27462
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

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

Affiliation:

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

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

3. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, New Territories, Hong Kong

Funder

National Key Research and Development Program of China

Hong Kong Research Grants Council under the Areas of Excellence scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10623554/10525198.pdf?arnumber=10525198

Reference45 articles.

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

2. Federated learning: Strategies for improving communication efficiency;Konečnỳ;arXiv:1610.05492,2016

3. Federated Machine Learning

4. Federated Learning in Mobile Edge Networks: A Comprehensive Survey

5. Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges

Cited by 1 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. Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects;IEEE Open Journal of the Communications Society;2024

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