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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:

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Moming Duan, Duo Liu, Xinyuan Ji, et al.
IEEE Transactions on Parallel and Distributed Systems (2021), pp. 1-1
Open Access | Times Cited: 66

Showing 1-25 of 66 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: 44
Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems
Renhao Lu, Weizhe Zhang, Yan Wang, et al.
IEEE Transactions on Parallel and Distributed Systems (2023) Vol. 34, Iss. 4, pp. 1145-1158
Closed Access | Times Cited: 41
Advances and Challenges in Meta-Learning: A Technical Review
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 7, pp. 4763-4779
Open Access | Times Cited: 36
On Model Transmission Strategies in Federated Learning With Lossy Communications
Xiaoxin Su, Yipeng Zhou, Laizhong Cui, et al.
IEEE Transactions on Parallel and Distributed Systems (2023), pp. 1-14
Closed Access | Times Cited: 19
Federated Learning for Predictive Maintenance and Anomaly Detection Using Time Series Data Distribution Shifts in Manufacturing Processes
Jisu Ahn, Younjeong Lee, Namji Kim, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7331-7331
Open Access | Times Cited: 16
Fed-RAC: Resource-Aware Clustering for Tackling Heterogeneity of Participants in Federated Learning
Rahul Mishra, Hari Prabhat Gupta, Garvit Banga, et al.
IEEE Transactions on Parallel and Distributed Systems (2024) Vol. 35, Iss. 7, pp. 1207-1220
Closed Access | Times Cited: 6
StoCFL: A stochastically clustered federated learning framework for Non-IID data with dynamic client participation
Dun Zeng, Xiangjing Hu, Shiyu Liu, et al.
Neural Networks (2025), pp. 107278-107278
Closed Access
Clustered Federated Learning: A Review
Majid Morafah, Mahdi Morafah
IntechOpen eBooks (2025)
Closed Access
FedAEB: Deep Reinforcement Learning Based Joint Client Selection and Resource Allocation Strategy for Heterogeneous Federated Learning
Feng Zheng, Yuze Sun, Bin Ni
IEEE Transactions on Vehicular Technology (2024) Vol. 73, Iss. 6, pp. 8835-8846
Closed Access | Times Cited: 4
Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous Federated smart grids
Habib Ullah Manzoor, Atif Jafri, Ahmed Zoha
Internet of Things (2024), pp. 101376-101376
Open Access | Times Cited: 4
Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching
Zexi Li, Jiaxun Lu, Shuang Luo, et al.
IEEE Transactions on Big Data (2022) Vol. 10, Iss. 6, pp. 812-826
Open Access | Times Cited: 20
FedEdge: Accelerating Edge-Assisted Federated Learning
Kaibin Wang, Qiang He, Feifei Chen, et al.
Proceedings of the ACM Web Conference 2022 (2023), pp. 2895-2904
Closed Access | Times Cited: 10
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
Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework
Xiao-Xiang Wei, Hua Huang
IEEE Transactions on Image Processing (2023) Vol. 32, pp. 995-1010
Closed Access | Times Cited: 9
Personalized Federated Few-Shot Learning
Yunfeng Zhao, Guoxian Yu, Jun Wang, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 35, Iss. 2, pp. 2534-2544
Closed Access | Times Cited: 13
A dynamic adaptive iterative clustered federated learning scheme
Run Du, Shuo Xu, Rui Zhang, et al.
Knowledge-Based Systems (2023) Vol. 276, pp. 110741-110741
Closed Access | Times Cited: 7
Clustered Federated Learning in Internet of Things: Convergence Analysis and Resource Optimization
Bo Xu, Wenchao Xia, Haitao Zhao, et al.
IEEE Internet of Things Journal (2023) Vol. 11, Iss. 2, pp. 3217-3232
Open Access | Times Cited: 7
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
High-Precision Cluster Federated Learning for Smart Home: An Edge-Cloud Collaboration Approach
Chao Li, Hui Yang, Zhengjie Sun, et al.
IEEE Access (2023) Vol. 11, pp. 102157-102168
Open Access | Times Cited: 7
Accelerating Federated Learning Via Parameter Selection and Pre-Synchronization in Mobile Edge-Cloud Networks
Huan Zhou, Mingze Li, Peng Sun, et al.
IEEE Transactions on Mobile Computing (2024) Vol. 23, Iss. 11, pp. 10313-10328
Closed Access | Times Cited: 2
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
Yasar Abbas Ur Rehman, Yan Gao, Pedro P. B. de Gusmão, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2023), pp. 16418-16427
Open Access | Times Cited: 5
Knowledge-Aware Parameter Coaching for Personalized Federated Learning
Mingjian Zhi, Yuanguo Bi, Wenchao Xu, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 15, pp. 17069-17077
Open Access | Times Cited: 1
Low-overhead Clustered Federated Learning for Personalized Stress Monitoring
Shiyi Jiang, Farshad Firouzi, Krishnendu Chakrabarty
(2023)
Open Access | Times Cited: 4
A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design
Mahdi Morafah, Weijia Wang, Bill Lin
IEEE Transactions on Artificial Intelligence (2023) Vol. 5, Iss. 4, pp. 1708-1717
Open Access | Times Cited: 4
Joint Contract Design and Task Reorganization for Semi-Decentralized Federated Edge Learning in Vehicular Networks
Bo Xu, Haitao Zhao, Haotong Cao, et al.
IEEE Transactions on Vehicular Technology (2024) Vol. 73, Iss. 7, pp. 10539-10553
Closed Access | Times Cited: 1
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