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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:

Privacy-aware service placement for mobile edge computing via federated learning
Yongfeng Qian, Long Hu, Jing Chen, et al.
Information Sciences (2019) Vol. 505, pp. 562-570
Closed Access | Times Cited: 109

Showing 1-25 of 109 citing articles:

Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, et al.
IEEE Communications Surveys & Tutorials (2020) Vol. 22, Iss. 3, pp. 2031-2063
Open Access | Times Cited: 1822
A review of applications in federated learning
Li Li, Yuxi Fan, Ying Kei Tse, et al.
Computers & Industrial Engineering (2020) Vol. 149, pp. 106854-106854
Open Access | Times Cited: 844
Machine Learning in IoT Security: Current Solutions and Future Challenges
Fatima Hussain, Rasheed Hussain, Syed Ali Hassan, et al.
IEEE Communications Surveys & Tutorials (2020) Vol. 22, Iss. 3, pp. 1686-1721
Open Access | Times Cited: 670
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, et al.
IEEE Transactions on Knowledge and Data Engineering (2021) Vol. 35, Iss. 4, pp. 3347-3366
Open Access | Times Cited: 655
A Comprehensive Survey of Privacy-preserving Federated Learning
Xuefei Yin, Yanming Zhu, Jiankun Hu
ACM Computing Surveys (2021) Vol. 54, Iss. 6, pp. 1-36
Open Access | Times Cited: 340
Resource Scheduling in Edge Computing: A Survey
Quyuan Luo, Shihong Hu, Changle Li, et al.
IEEE Communications Surveys & Tutorials (2021) Vol. 23, Iss. 4, pp. 2131-2165
Open Access | Times Cited: 314
Federated learning review: Fundamentals, enabling technologies, and future applications
Syreen Banabilah, Moayad Aloqaily, Eitaa Alsayed, et al.
Information Processing & Management (2022) Vol. 59, Iss. 6, pp. 103061-103061
Closed Access | Times Cited: 275
A federated learning system with enhanced feature extraction for human activity recognition
Zhiwen Xiao, Xin Xu, Huanlai Xing, et al.
Knowledge-Based Systems (2021) Vol. 229, pp. 107338-107338
Closed Access | Times Cited: 195
A Systematic Literature Review on Federated Machine Learning
Sin Kit Lo, Qinghua Lu, Chen Wang, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 5, pp. 1-39
Open Access | Times Cited: 130
Federated Neural Architecture Search for Medical Data Security
Xin Liu, Jianwei Zhao, Jie Li, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 18, Iss. 8, pp. 5628-5636
Closed Access | Times Cited: 118
Applying machine learning techniques for caching in next-generation edge networks: A comprehensive survey
Junaid Shuja, Kashif Bilal, Waleed Alasmary, et al.
Journal of Network and Computer Applications (2021) Vol. 181, pp. 103005-103005
Open Access | Times Cited: 117
Federated learning in smart cities: Privacy and security survey
Rasha Al-Huthaifi, Tianrui Li, Wei Huang, et al.
Information Sciences (2023) Vol. 632, pp. 833-857
Closed Access | Times Cited: 68
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu, et al.
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 96
Hybrid Blockchain-Based Resource Trading System for Federated Learning in Edge Computing
Sizheng Fan, Hongbo Zhang, Yuchen Zeng, et al.
IEEE Internet of Things Journal (2020) Vol. 8, Iss. 4, pp. 2252-2264
Closed Access | Times Cited: 91
Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning
Tianqing Zhu, Wei Zhou, Dayong Ye, et al.
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 2, pp. 1414-1426
Closed Access | Times Cited: 73
Mechanisms for Resource Allocation and Pricing in Mobile Edge Computing Systems
Tayebeh Bahreini, Hossein Badri, Daniel Grosu
IEEE Transactions on Parallel and Distributed Systems (2021) Vol. 33, Iss. 3, pp. 667-682
Closed Access | Times Cited: 69
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
Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review
Hadi Tabatabaee Malazi, Saqib Rasool Chaudhry, Aqeel Kazmi, et al.
IEEE Access (2022) Vol. 10, pp. 32639-32688
Open Access | Times Cited: 56
Blockchain-based Federated Learning: A Comprehensive Survey
Zhilin Wang, Qin Hu, Minghui Xu, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 55
A review on federated learning towards image processing
Fahad Ahmed Khokhar, Jamal Hussain Shah, Muhammad Attique Khan, et al.
Computers & Electrical Engineering (2022) Vol. 99, pp. 107818-107818
Closed Access | Times Cited: 50
Federated Learning and Its Role in the Privacy Preservation of IoT Devices
Tanweer Alam, Ruchi Gupta
Future Internet (2022) Vol. 14, Iss. 9, pp. 246-246
Open Access | Times Cited: 50
Enabling technologies for AI empowered 6G massive radio access networks
Md. Shahjalal, Woojun Kim, Waqas Khalid, et al.
ICT Express (2022) Vol. 9, Iss. 3, pp. 341-355
Open Access | Times Cited: 47
Security and Privacy Threats to Federated Learning: Issues, Methods, and Challenges
Junpeng Zhang, Hui Zhu, Fengwei Wang, et al.
Security and Communication Networks (2022) Vol. 2022, pp. 1-24
Open Access | Times Cited: 45
A Review on Federated Learning and Machine Learning Approaches: Categorization, Application Areas, and Blockchain Technology
Roseline Oluwaseun Ogundokun, Sanjay Misra, Rytis Maskeliūnas, et al.
Information (2022) Vol. 13, Iss. 5, pp. 263-263
Open Access | Times Cited: 44
Machine learning methods for service placement: a systematic review
Parviz Keshavarz Haddadha, Mohammad Hossein Rezvani, Mahdi Mollamotalebi, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 3
Open Access | Times Cited: 13
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