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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:

FedAda: Fast-convergent adaptive federated learning in heterogeneous mobile edge computing environment
Jinghui Zhang, Xinyu Cheng, Cheng Wang, et al.
World Wide Web (2022) Vol. 25, Iss. 5, pp. 1971-1998
Closed Access | Times Cited: 25

Showing 25 citing articles:

A survey on federated learning: challenges and applications
Jie Wen, Zhixia Zhang, Y. Lan, et al.
International Journal of Machine Learning and Cybernetics (2022) Vol. 14, Iss. 2, pp. 513-535
Open Access | Times Cited: 204
A state-of-the-art survey on solving non-IID data in Federated Learning
Xiaodong Ma, Jia Zhu, Zhihao Lin, et al.
Future Generation Computer Systems (2022) Vol. 135, pp. 244-258
Closed Access | Times Cited: 189
Federated Learning for Edge Computing: A Survey
Alexander Brecko, Erik Kajáti, Jiří Koziorek, et al.
Applied Sciences (2022) Vol. 12, Iss. 18, pp. 9124-9124
Open Access | Times Cited: 62
Managing Distributed Machine Learning Lifecycle for Healthcare Data in the Cloud
Engin Zeydan, Şuayb Ş. Arslan, Madhusanka Liyanage
IEEE Access (2024) Vol. 12, pp. 115750-115774
Open Access | Times Cited: 4
Weighted Average Consensus Algorithms in Distributed and Federated Learning
Bernardo Camajori Tedeschini, Stefano Savazzi, Monica Nicoli
IEEE Transactions on Network Science and Engineering (2025) Vol. 12, Iss. 2, pp. 1369-1382
Closed Access
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
Tactile internet of federated things: Toward fine-grained design of FL-based architecture to meet TIoT demands
Omar Alnajar, Ahmed Barnawi
Computer Networks (2023) Vol. 231, pp. 109712-109712
Closed Access | Times Cited: 7
Time-Sensitive Federated Learning With Heterogeneous Training Intensity: A Deep Reinforcement Learning Approach
Weijian Pan, Xiumin Wang, Pan Zhou, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2024) Vol. 8, Iss. 2, pp. 1402-1415
Closed Access | Times Cited: 2
AsyFed: Accelerated Federated Learning With Asynchronous Communication Mechanism
Zhixin Li, Chunpu Huang, Keke Gai, et al.
IEEE Internet of Things Journal (2022) Vol. 10, Iss. 10, pp. 8670-8683
Closed Access | Times Cited: 11
Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization
Jinghui Zhang, Jiawei Wang, Yaning Li, et al.
ACM Transactions on Sensor Networks (2023) Vol. 20, Iss. 2, pp. 1-32
Closed Access | Times Cited: 6
ASCFL: Accurate and Speedy Semi-Supervised Clustering Federated Learning
Jingyi He, Biyao Gong, Jiadi Yang, et al.
Tsinghua Science & Technology (2023) Vol. 28, Iss. 5, pp. 823-837
Open Access | Times Cited: 5
Understanding global aggregation and optimization of federated learning
Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel, Gang Li
Future Generation Computer Systems (2024) Vol. 159, pp. 114-133
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
Heterogeneous Training Intensity for Federated Learning: A Deep Reinforcement Learning Approach
Manying Zeng, Xiumin Wang, Weijian Pan, et al.
IEEE Transactions on Network Science and Engineering (2022) Vol. 10, Iss. 2, pp. 990-1002
Closed Access | Times Cited: 5
Predicting the individual effects of team competition on college students’ academic performance in mobile edge computing
Huiling Zhang, Huatao Wu, Zhengde Li, et al.
Journal of Cloud Computing Advances Systems and Applications (2024) Vol. 13, Iss. 1
Open Access
Case studies and recommendations for designing federated learning models for digital healthcare systems
Chunying Wu, Pushpanjali Gupta, Sulagna Mohapatra
Elsevier eBooks (2024), pp. 301-323
Closed Access
Staleness aware semi-asynchronous federated learning
Miri Yu, Jiheon Choi, Jae-Hyun Lee, et al.
Journal of Parallel and Distributed Computing (2024) Vol. 193, pp. 104950-104950
Closed Access
FedCA: Efficient Federated Learning with Client Autonomy
Lv Na, Zhi Shen, Chen Chen, et al.
(2024), pp. 494-503
Closed Access
MetaClusterFL: Personalized Federated Learning on Non-IID data with Meta-learning and Clustering
Hui Zeng, Shiyu Xiong, Hongzhou Shi
2022 International Joint Conference on Neural Networks (IJCNN) (2024) Vol. 33, pp. 1-10
Closed Access
MobFedLS: A framework to provide federated learning for mobile nodes in V2X environments
Bernardo Barreto, Carlos Senna, Pedro Rito, et al.
Future Generation Computer Systems (2024) Vol. 163, pp. 107514-107514
Open Access
Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning
Aulia Arif Wardana, Parman Sukarno
ACM Computing Surveys (2024) Vol. 57, Iss. 4, pp. 1-36
Open Access
Machine Learning based Analysis of the Effect of Team Competition on College Students’ Academic Performance
Huiling Zhang, Huatao Wu, Zhengde Li, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning?
Seungjun Lee, Miri Yu, Daegun Yoon, et al.
2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (2023), pp. 799-808
Closed Access | Times Cited: 1
Metaclusterfl: Personalized Federated Learning on Non-Iid Data with Meta-Learning and Clustering
Hui Zeng, Shiyu Xiong, Hongzhou Shi
(2023)
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
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