Client Selection in Federated Learning: Principles, Challenges, and Opportunities-Reference-Cited by-同舟云学术

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Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Published:2023-12-15 Issue:24 Volume:10 Page:21811-21819
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Fu Lei¹,Zhang Huanle²^ORCID,Gao Ge²,Zhang Mi³,Liu Xin⁴^ORCID

Affiliation:

1. Center for Capital Operation, Bank of Jiangsu, Nanjing, China

2. School of Computer Science and Technology, Shandong University, Jinan, China

3. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

4. Department of Computer Science, University of California at Davis, Davis, CA, USA

Funder

Jiangsu Funding Program for Excellent Postdoctoral Talent

National Science Foundation of the United States

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

https://ieeexplore.ieee.org/ielam/6488907/10353047/10197174-aam.pdf

Reference85 articles.

1. Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities

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

3. Federated learning for mobile keyboard prediction;Hard;arXiv:1811.03604,2019

4. Federated Learning for Healthcare: Systematic Review and Architecture Proposal

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1. Federated learning energy saving through client selection;Pervasive and Mobile Computing;2024-10

2. Fed-RHLP: Enhancing Federated Learning with Random High-Local Performance Client Selection for Improved Convergence and Accuracy;Symmetry;2024-09-09

3. A comprehensive survey on client selection strategies in federated learning;Computer Networks;2024-09

4. Client Selection in Hierarchical Federated Learning;IEEE Internet of Things Journal;2024-09-01

5. FedAvg-P: Performance-Based Hierarchical Federated Learning-Based Anomaly Detection System Aggregation Strategy for Advanced Metering Infrastructure;Sensors;2024-08-24

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