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:

A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Ziyao Liu, Yu Jiang, Jiyuan Shen, et al.
ACM Computing Surveys (2024) Vol. 57, Iss. 1, pp. 1-38
Open Access | Times Cited: 8

Showing 8 citing articles:

Dual-domain based backdoor attack against federated learning
Guorui Li, Ray-I Chang, Ying Wang, et al.
Neurocomputing (2025), pp. 129424-129424
Closed Access | Times Cited: 1

A survey of security and privacy issues of machine unlearning
Aurelia Chen, Yangyi Li, Chenxu Zhao, et al.
AI Magazine (2025) Vol. 46, Iss. 1
Open Access

Enhancing AI safety of machine unlearning for ensembled models
Huanyi Ye, Jiale Guo, Ziyao Liu, et al.
Applied Soft Computing (2025), pp. 113011-113011
Closed Access

Is Client Unlearning Really Necessary in Federating Learning?
Alessio Mora, Paolo Bellavista
(2025), pp. 0696-0701
Closed Access

Class-wise federated unlearning: Harnessing active forgetting with teacher-student memory generation
Yuyuan Li, Jiaming Zhang, Yixiu Liu, et al.
Knowledge-Based Systems (2025), pp. 113353-113353
Closed Access

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
Yu Jiang, Xindi Tong, Ziyao Liu, et al.
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 7822-7831
Open Access

FedUNRAN: On-device Federated Unlearning via Random Labels
Alessio Mora, Luca Dominici, Paolo Bellavista
2021 IEEE International Conference on Big Data (Big Data) (2024), pp. 7955-7960
Closed Access

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