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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:

Adaptive Clustered Federated Learning for Heterogeneous Data in Edge Computing
Biyao Gong, Tianzhang Xing, Zhidan Liu, et al.
Mobile Networks and Applications (2022) Vol. 27, Iss. 4, pp. 1520-1530
Closed Access | Times Cited: 21

Showing 21 citing articles:

Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions
Qiang Duan, Jun Huang, Shijing Hu, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 25, Iss. 4, pp. 2892-2950
Closed Access | Times Cited: 47
Federated Learning Based on CTC for Heterogeneous Internet of Things
Demin Gao, Haoyu Wang, Xiuzhen Guo, et al.
IEEE Internet of Things Journal (2023) Vol. 10, Iss. 24, pp. 22673-22685
Closed Access | Times Cited: 24
dy-TACFL: Dynamic Temporal Adaptive Clustered Federated Learning for Heterogeneous Clients
Syed Saqib Ali, Mazhar Ali, Dost Muhammad Saqib Bhatti, et al.
Electronics (2025) Vol. 14, Iss. 1, pp. 152-152
Open Access
An Energy-Efficient Decentralized Federated Learning Framework for Mobile-IoT Networks
Nastooh Taheri Javan, Elahe Zakizadeh Gharyeali, Seyedakbar Mostafavi
Computer Networks (2025), pp. 111233-111233
Closed Access
Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0
Ali Akbar Siddique, Wadii Boulila, Mohammed S. Alshehri, et al.
IEEE Transactions on Consumer Electronics (2023) Vol. 70, Iss. 1, pp. 1923-1939
Closed Access | Times Cited: 10
Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity
Zoujing Yao, Pengyu Song, Chunhui Zhao
Journal of Process Control (2023) Vol. 129, pp. 103038-103038
Closed Access | Times Cited: 7
Clustered Federated Learning Based on Momentum Gradient Descent for Heterogeneous Data
Xiaoyi Zhao, Ping Xie, Ling Xing, et al.
Electronics (2023) Vol. 12, Iss. 9, pp. 1972-1972
Open Access | Times Cited: 5
FedEem: a fairness-based asynchronous federated learning mechanism
Wei Gu, Yifan Zhang
Journal of Cloud Computing Advances Systems and Applications (2023) Vol. 12, Iss. 1
Open Access | Times Cited: 5
Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study
Basmah K. Alotaibi, Fakhri Alam Khan, Sajjad Mahmood
Applied Sciences (2024) Vol. 14, Iss. 7, pp. 2720-2720
Open Access | Times Cited: 1
Heterogeneous Resources in Infrastructures of the Edge Network Paradigm: A Comprehensive Review
Qusay S. Alsaffar, Leila Jemni Ben Ayed
Karbala International Journal of Modern Science (2024) Vol. 10, Iss. 2
Open Access | Times Cited: 1
LayerCFL: an efficient federated learning with layer-wised clustering
Jie Yuan, Rui Qian, Tingting Yuan, et al.
Cybersecurity (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 3
Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions
Qiang Duan, Jun Huang, Shijing Hu, et al.
(2022)
Open Access | Times Cited: 4
Fed-SHARC: Resilient Decentralized Federated Learning based on Reward driven Clustering
Renuga Kanagavelu, Chris George Anil, Yuan Wang, et al.
(2024), pp. 581-586
Closed Access
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
Md Sirajul Islam, Simin Javaherian, Fei Xu, et al.
(2024), pp. 474-483
Open Access
Robust and Scalable Federated Learning Framework for Client Data Heterogeneity Based on Optimal Clustering
Zihan Li, Shuai Yuan, Zhitao Guan
Journal of Parallel and Distributed Computing (2024), pp. 104990-104990
Closed Access
Federated Learning with dataset splitting and weighted mean using Particle Swarm Optimization
Mohit Agarwal, Garima Jaiswal, Rohit Kumar Kaliyar, et al.
IEEE Access (2024) Vol. 12, pp. 161968-161981
Open Access
One-Shot Clustering for Federated Learning
Maciej Krzysztof Zuziak, Roberto Pellungrini, Salvatore Rinzivillo
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 8108-8117
Closed Access
Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for Distributed Edge Network
Ningyu An, Xiao Liang, Fei Zhou, et al.
Communications in computer and information science (2023), pp. 91-104
Closed Access
ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data
Z. Z. Li, Zhitao Guan, Shuai Yuan, et al.
(2023), pp. 259-264
Closed Access
Parameter-Efficient Federated Learning for Edge Computing with End Devices Resource Limitation
Ying Qian, Lianbo Ma
2022 4th International Conference on Industrial Artificial Intelligence (IAI) (2022), pp. 1-5
Closed Access
Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions
Qiang Duan, Jun Huang, Shijing Hu, et al.
(2022)
Open Access
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