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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:

Communication-Efficient Federated Learning for Anomaly Detection in Industrial Internet of Things
Yi Liu, Neeraj Kumar, Zehui Xiong, et al.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference (2020), pp. 1-6
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges
Abbas Shah Syed, Daniel Sierra-Sosa, Anup Kumar, et al.
Smart Cities (2021) Vol. 4, Iss. 2, pp. 429-475
Open Access | Times Cited: 361
Federated learning for malware detection in IoT devices
Valerian Rey, Pedro Miguel Sánchez Sánchez, Alberto Huertas Celdrán, et al.
Computer Networks (2022) Vol. 204, pp. 108693-108693
Open Access | Times Cited: 249
Fusion of Federated Learning and Industrial Internet of Things: A survey
M. Parimala, Swarna Priya Ramu, Quoc‐Viet Pham, et al.
Computer Networks (2022) Vol. 212, pp. 109048-109048
Open Access | Times Cited: 189
Differential Privacy for Deep and Federated Learning: A Survey
Ahmed El Ouadrhiri, Ahmed Abdelhadi
IEEE Access (2022) Vol. 10, pp. 22359-22380
Open Access | Times Cited: 186
Graph Neural Networks for Anomaly Detection in Industrial Internet of Things
Yulei Wu, Hong‐Ning Dai, Haina Tang
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 12, pp. 9214-9231
Open Access | Times Cited: 171
Anomaly Detection in Blockchain Networks: A Comprehensive Survey
Muneeb Ul Hassan, Mubashir Husain Rehmani, Jinjun Chen
IEEE Communications Surveys & Tutorials (2022) Vol. 25, Iss. 1, pp. 289-318
Open Access | Times Cited: 160
Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning
Xiaoding Wang, Sahil Garg, Hui Lin, et al.
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 10, pp. 7110-7119
Closed Access | Times Cited: 147
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
Longling Zhang, Bochen Shen, Ahmed Barnawi, et al.
Information Systems Frontiers (2021) Vol. 23, Iss. 6, pp. 1403-1415
Open Access | Times Cited: 78
A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection
Marta Catillo, Antonio Pecchia, Umberto Villano
Applied Sciences (2023) Vol. 13, Iss. 2, pp. 837-837
Open Access | Times Cited: 24
Security and privacy of industrial big data: Motivation, opportunities, and challenges
Naveed Anjum, Zohaib Latif, Hongsong Chen
Journal of Network and Computer Applications (2025) Vol. 237, pp. 104130-104130
Closed Access | Times Cited: 1
Federated learning-empowered smart manufacturing and product lifecycle management: A review
Jiewu Leng, Richard Li, Junxing Xie, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103179-103179
Closed Access | Times Cited: 1
When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries
Wei Yang Bryan Lim, Zehui Xiong, Jiawen Kang, et al.
IEEE Transactions on Industrial Informatics (2020) Vol. 18, Iss. 1, pp. 457-466
Open Access | Times Cited: 61
Federated Learning: Navigating the Landscape of Collaborative Intelligence
Konstantinos Lazaros, Dimitrios E. Koumadorakis, Aristidis G. Vrahatis, et al.
Electronics (2024) Vol. 13, Iss. 23, pp. 4744-4744
Open Access | Times Cited: 6
Federated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks
Suwani Jayasinghe, Yushan Siriwardhana, Pawani Porambage, et al.
(2022)
Open Access | Times Cited: 24
Edge-assisted federated learning for anomaly detection in diverse IoT network
Priya Sharma, Sanjay Kumar Sharma, Diksha Dani
International Journal of Information Technology (2024)
Closed Access | Times Cited: 5
A novel federated learning aggregation algorithm for AIoT intrusion detection
Yidong Jia, Fuhong Lin, Yan Sun
IET Communications (2024) Vol. 18, Iss. 7, pp. 429-436
Open Access | Times Cited: 4
Privacy-Preserving Intrusion Detection Using FedSplit Learning
Malika Abid, Mohammed Kamel Benkaddour, Mohamed Benouis
Lecture notes in networks and systems (2025), pp. 29-42
Closed Access
Decentralized Federated Learning Preserves Model and Data Privacy
Thorsten Wittkopp, Alexander Acker
Lecture notes in computer science (2021), pp. 176-187
Closed Access | Times Cited: 22
Peer-to-Peer Federated Learning Based Anomaly Detection for Open Radio Access Networks
Dinaj Attanayaka, Pawani Porambage, Madhusanka Liyanage, et al.
ICC 2022 - IEEE International Conference on Communications (2023)
Closed Access | Times Cited: 9
Defending Federated Learning from Backdoor Attacks: Anomaly-Aware FedAVG with Layer-Based Aggregation
Habib Ullah Manzoor, Ahsan Raza Khan, Tahir Sher, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 8
Edge Computing and Federated Learning for Real-Time Anomaly Detection in Industrial Internet of Things (IIoT)
Shivkumar V Haldikar, Omer Farook Mohideen Abdul Kader, Roop Kumar Yekollu
2022 International Conference on Inventive Computation Technologies (ICICT) (2024)
Closed Access | Times Cited: 3
Resource-Constrained Federated Edge Learning With Heterogeneous Data: Formulation and Analysis
Yi Liu, Yuanshao Zhu, James J. Q. Yu
IEEE Transactions on Network Science and Engineering (2021) Vol. 9, Iss. 5, pp. 3166-3178
Open Access | Times Cited: 20
Anomaly detection of industrial state quantity time-Series data based on correlation and long short-term memory
Mingxin Tang, Wei Chen, Wen Yang
Connection Science (2022) Vol. 34, Iss. 1, pp. 2048-2065
Open Access | Times Cited: 14
Decision-making for the anomalies in IIoTs based on 1D convolutional neural networks and Dempster–Shafer theory (DS-1DCNN)
Tuğrul Çavdar, Nader Ebrahimpour, Muhammet Talha Kakız, et al.
The Journal of Supercomputing (2022) Vol. 79, Iss. 2, pp. 1683-1704
Closed Access | Times Cited: 14
Resource Aware Long Short-Term Memory Model (RALSTMM) Based On-Device Incremental Learning for Industrial Internet of Things
Atallo Kassaw Takele, Balázs Villányi
IEEE Access (2023) Vol. 11, pp. 63107-63115
Open Access | Times Cited: 7
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