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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Contribution‐based Federated Learning client selection
Weiwei Lin, Yinhai Xu, Bo Liu, et al.
International Journal of Intelligent Systems (2022) Vol. 37, Iss. 10, pp. 7235-7260
Open Access | Times Cited: 25

Showing 25 citing articles:

A Systematic Literature Review on Client Selection in Federated Learning
Carl Smestad, Jingyue Li
(2023), pp. 2-11
Open Access | Times Cited: 15
A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods
Naghmeh Khajehali, Jun Yan, Yang-Wai Chow, et al.
Sensors (2023) Vol. 23, Iss. 16, pp. 7235-7235
Open Access | Times Cited: 13
Model architecture level privacy leakage in neural networks
Yan Li, Hongyang Yan, Teng Huang, et al.
Science China Information Sciences (2023) Vol. 67, Iss. 3
Closed Access | Times Cited: 12
Communication-Efficient Federated Learning for Heterogeneous Clients
Ying Li, Xingwei Wang, Haodong Li, et al.
ACM Transactions on Internet Technology (2025)
Closed Access
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment
Tatjana Legler, Vinit Hegiste, Ahmed Anwar, et al.
Procedia Computer Science (2025) Vol. 253, pp. 2831-2840
Open Access
IntFedSV: A Novel Participants’ Contribution Evaluation Mechanism for Federated Learning
Tianxu Cui, Ying Shi, Wenge Li, et al.
International Journal of Intelligent Systems (2025) Vol. 2025, Iss. 1
Open Access
Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations
Mehreen Tahir, Muhammad Intizar Ali
Studies in computational intelligence (2025), pp. 43-62
Closed Access
Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains
Ιοannis Schoinas, Anna Triantafyllou, Dimosthenis Ioannidis, et al.
IEEE Open Journal of the Communications Society (2024) Vol. 5, pp. 5933-6017
Open Access | Times Cited: 3
Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation
Yong-gyom Kim, S.H. Yang, Kang Yoon Lee
Applied Sciences (2024) Vol. 14, Iss. 16, pp. 6876-6876
Open Access | Times Cited: 2
FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling
He Yang, Wei Xi, Zizhao Wang, et al.
Information Sciences (2023) Vol. 645, pp. 119360-119360
Closed Access | Times Cited: 5
FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning
Shaohua Cao, Hanqing Zhang, Tian Wen, et al.
High-Confidence Computing (2023) Vol. 4, Iss. 2, pp. 100179-100179
Open Access | Times Cited: 5
FedQL: Q-Learning Guided Aggregation for Federated Learning
Cao Mei, Mengying Zhao, Tingting Zhang, et al.
Lecture notes in computer science (2024), pp. 263-282
Closed Access | Times Cited: 1
Multi-Criterion Client Selection for Efficient Federated Learning
Mehreen Tahir, Muhammad Intizar Ali
Proceedings of the AAAI Symposium Series (2024) Vol. 3, Iss. 1, pp. 318-322
Open Access | Times Cited: 1
Adaptive Idle Model Fusion in Hierarchical Federated Learning for Unbalanced Edge Regions
Jiuyun Xu, Hanfei Fan, Qiqi Wang, et al.
IEEE Transactions on Network Science and Engineering (2024) Vol. 11, Iss. 5, pp. 4603-4616
Closed Access | Times Cited: 1
Client Selection Frameworks Within Federated Machine Learning: The Current Paradigm
Lincoln R. Best, Ernest Foo, Hui Tian, et al.
Smart sensors, measurement and instrumentation (2023), pp. 61-83
Closed Access | Times Cited: 1
FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated Learning
Shuming Fan, Chuanjia Wang, X.Y. Ruan, et al.
ACM Transactions on Asian and Low-Resource Language Information Processing (2024)
Open Access
Technical considerations of federated learning in digital healthcare systems
Emmanuel C. Alozie, Hawau I. Olagunju, Nasir Faruk, et al.
Elsevier eBooks (2024), pp. 237-282
Closed Access
The Analysis and Optimization of Volatile Clients in Over-the-Air Federated Learning
Fang Shi, Weiwei Lin, Xiumin Wang, et al.
IEEE Transactions on Mobile Computing (2024) Vol. 23, Iss. 12, pp. 13144-13157
Closed Access
RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates
Shuming Fan, Hongjian Shi, Chuanjia Wang, et al.
ACM Transactions on Internet Technology (2024)
Open Access
Fuzzy-Based Client Selection for Enhanced Model Convergence in Federated Learning
Yulong Zhou, Ruiming Bao
2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE) (2024), pp. 1323-1327
Closed Access
Node and relevant data selection in distributed predictive analytics: A query-centric approach
Tahani Aladwani, Christos Anagnostopoulos, Kostas Kolomvatsos
Journal of Network and Computer Applications (2024), pp. 104029-104029
Open Access
Query-driven Edge Node Selection in Distributed Learning Environments
Tahani Aladwani, Christos Anagnostopoulos, Kostas Kolomvatsos, et al.
(2023), pp. 146-153
Open Access
XFed: Improving Explainability in Federated Learning by Intersection Over Union Ratio Extended Client Selection
Juan Zhao, Yuankai Zhang, Ruixuan Li, et al.
Frontiers in artificial intelligence and applications (2023)
Open Access
An Efficient Client Selection for Wireless Federated Learning
Jingyi Chen, Qiang Wang, Wenqi Zhang
2022 27th Asia Pacific Conference on Communications (APCC) (2023), pp. 291-296
Closed Access
Clustering-Based Federated Learning for Heterogeneous IoT Data
Shu-Min Li, Linna Wei, Weidong Zhang, et al.
(2023), pp. 172-179
Closed Access
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