Context-Aware Online Client Selection for Hierarchical Federated Learning-Reference-Cited by-同舟云学术

Context-Aware Online Client Selection for Hierarchical Federated Learning

Published:2022-12-01 Issue:12 Volume:33 Page:4353-4367
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Qu Zhe¹^ORCID,Duan Rui¹,Chen Lixing²^ORCID,Xu Jie³^ORCID,Lu Zhuo¹^ORCID,Liu Yao⁴^ORCID

Affiliation:

1. Department of Electrical Engineering, University of South Florida, Tampa, FL, USA

2. Institute of Cyber Science and Technology, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA

4. Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

https://ieeexplore.ieee.org/ielam/71/9790018/9809926-aam.pdf

Reference48 articles.

1. Optimizing Federated Learning in Distributed Industrial IoT: A Multi-Agent Approach

2. V12. 1: User's manual for CPLEX;cplex;International Business Machines Corporation,2009

3. Federated Learning over Wireless Networks: Optimization Model Design and Analysis

4. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

5. Low-latency federated learning over wireless channels with differential privacy;wei,2021

Cited by 37 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. Auction-based client selection for online Federated Learning;Information Fusion;2024-12

2. Reliable incentive mechanism in hierarchical federated learning based on two-way reputation and contract theory;Future Generation Computer Systems;2024-10

3. A Cooperative Analysis to Incentivize Communication-Efficient Federated Learning;IEEE Transactions on Mobile Computing;2024-10

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

5. Adaptive Idle Model Fusion in Hierarchical Federated Learning for Unbalanced Edge Regions;IEEE Transactions on Network Science and Engineering;2024-09