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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:

Federated Learning in Edge Computing: A Systematic Survey
Haftay Gebreslasie Abreha, Mohammad Hayajneh, Mohamed Adel Serhani
Sensors (2022) Vol. 22, Iss. 2, pp. 450-450
Open Access | Times Cited: 170

Showing 1-25 of 170 citing articles:

FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments
Shinu M. Rajagopal, M. Supriya, Rajkumar Buyya
Internet of Things (2023) Vol. 22, pp. 100784-100784
Open Access | Times Cited: 59
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
A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)
Chellammal Surianarayanan, John Jeyasekaran Lawrence, Pethuru Raj Chelliah, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1279-1279
Open Access | Times Cited: 41
A comprehensive review on federated learning based models for healthcare applications
Shagun Sharma, Kalpna Guleria
Artificial Intelligence in Medicine (2023) Vol. 146, pp. 102691-102691
Closed Access | Times Cited: 41
Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core
Joohyung Lee, Faranaksadat Solat, Tae Yeon Kim, et al.
IEEE Communications Surveys & Tutorials (2024) Vol. 26, Iss. 3, pp. 2176-2212
Closed Access | Times Cited: 22
A federated learning framework for cyberattack detection in vehicular sensor networks
Maha Driss, Iman Almomani, Zil e Huma, et al.
Complex & Intelligent Systems (2022) Vol. 8, Iss. 5, pp. 4221-4235
Open Access | Times Cited: 54
Federated learning in cloud-edge collaborative architecture: key technologies, applications and challenges
Guanming Bao, Ping Guo
Journal of Cloud Computing Advances Systems and Applications (2022) Vol. 11, Iss. 1
Open Access | Times Cited: 47
Reinforcement learning for intelligent online computation offloading in wireless powered edge networks
Ehzaz Mustafa, Junaid Shuja, Kashif Bilal, et al.
Cluster Computing (2022) Vol. 26, Iss. 2, pp. 1053-1062
Closed Access | Times Cited: 37
Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications
Prohim Tam, Riccardo Corrado, Chanthol Eang, et al.
Applied Sciences (2023) Vol. 13, Iss. 5, pp. 3083-3083
Open Access | Times Cited: 29
Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G
Adeb Salh, Razali Ngah, Lukman Audah‏, et al.
IEEE Access (2023) Vol. 11, pp. 16353-16367
Open Access | Times Cited: 26
FedMicro-IDA: A federated learning and microservices-based framework for IoT data analytics
Safa Ben Atitallah, Maha Driss, Henda Ben Ghézala
Internet of Things (2023) Vol. 23, pp. 100845-100845
Closed Access | Times Cited: 22
A collective AI via lifelong learning and sharing at the edge
Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, et al.
Nature Machine Intelligence (2024) Vol. 6, Iss. 3, pp. 251-264
Open Access | Times Cited: 10
FTSDC: A novel federated transfer learning strategy for bearing cross-machine fault diagnosis based on dual-correction training
Zhenhao Yan, Zifeng Xu, Yixiang Zhang, et al.
Advanced Engineering Informatics (2024) Vol. 61, pp. 102499-102499
Closed Access | Times Cited: 9
Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis
Lilan Liu, Zhenhao Yan, Tingting Zhang, et al.
Knowledge-Based Systems (2024) Vol. 291, pp. 111587-111587
Open Access | Times Cited: 8
A Comprehensive Survey on Load Forecasting Hybrid Models: Navigating the Futuristic Demand Response Patterns through Experts and Intelligent Systems
Kinza Fida, Usman Abbasi, Muhammad Adnan, et al.
Results in Engineering (2024) Vol. 23, pp. 102773-102773
Open Access | Times Cited: 7
Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning
Yuan Shi, Xianze Xu
Sensors (2022) Vol. 22, Iss. 9, pp. 3264-3264
Open Access | Times Cited: 34
Resource Management in Mobile Edge Computing: A Comprehensive Survey
Xiaojie Zhang, Saptarshi Debroy
ACM Computing Surveys (2023) Vol. 55, Iss. 13s, pp. 1-37
Open Access | Times Cited: 20
FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE
Aristeidis Karras, Αναστάσιος Γιάνναρος, Leonidas Theodorakopoulos, et al.
Electronics (2023) Vol. 12, Iss. 22, pp. 4633-4633
Open Access | Times Cited: 18
Enabling federated learning across the computing continuum: Systems, challenges and future directions
Cédric Prigent, Alexandru Costan, Gabriel Antoniu, et al.
Future Generation Computer Systems (2024) Vol. 160, pp. 767-783
Closed Access | Times Cited: 6
Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities
Ruqiang Yan, Weihua Li, Siliang Lu, et al.
Journal of Dynamics Monitoring and Diagnostics (2024)
Open Access | Times Cited: 5
A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study
Ahmed A. Ismail, Nour Eldeen M. Khalifa, Reda A. El-Khoribi
Cluster Computing (2025) Vol. 28, Iss. 3
Open Access
Cloud-to-things continuum: technical challenges and economical opportunities
Yacine Atif, Sujith Samuel Mathew
Elsevier eBooks (2025), pp. 3-61
Closed Access
Performance Evaluation of Convolutional Neural Network (CNN) for Skin Cancer Detection on Edge Computing Devices
Vincent, Garry Darian, Nico Surantha
Applied Sciences (2025) Vol. 15, Iss. 6, pp. 3077-3077
Open Access
Federated reinforcement learning approach for detecting uncertain deceptive target using autonomous dual UAV system
Haythem Bany Salameh, Mohannad Alhafnawi, Ala’eddin Masadeh, et al.
Information Processing & Management (2022) Vol. 60, Iss. 2, pp. 103149-103149
Closed Access | Times Cited: 22
Experimental Evaluation and Analysis of Federated Learning in Edge Computing Environments
Pham Khanh Quan, Majid Kundroo, Taehong Kim
IEEE Access (2023) Vol. 11, pp. 33628-33639
Open Access | Times Cited: 14
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