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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:

FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, et al.
Energy and AI (2023) Vol. 14, pp. 100271-100271
Open Access | Times Cited: 11

Showing 11 citing articles:

Deep learning for intelligent demand response and smart grids: A comprehensive survey
Prabadevi Boopathy, Madhusanka Liyanage, N. Deepa, et al.
Computer Science Review (2024) Vol. 51, pp. 100617-100617
Open Access | Times Cited: 18
A fog-edge-enabled intrusion detection system for smart grids
Noshina Tariq, Amjad Alsirhani, Mamoona Humayun, et al.
Journal of Cloud Computing Advances Systems and Applications (2024) Vol. 13, Iss. 1
Open Access | Times Cited: 10
Distributed Data Privacy Protection via Collaborative Anomaly Detection
Fei Zeng, Mingshen Wang, Yi Pan, et al.
Electronics (2025) Vol. 14, Iss. 2, pp. 295-295
Open Access
Investigating the Efficiency of a Federated Learning-Based Intrusion Detection System for Smart Grid
Najet Hamdi
SN Computer Science (2025) Vol. 6, Iss. 3
Closed Access
Detection of False Data Injection Attacks in Distribution Networks: A Vertical Federated Learning Approach
Mert Kesici, Bikash C. Pal, Guangya Yang
IEEE Transactions on Smart Grid (2024) Vol. 15, Iss. 6, pp. 5952-5964
Open Access | Times Cited: 4
Decentralized asynchronous adaptive federated learning algorithm for securely prediction of distributed power data
Qiang Li, Di Liu, Hui Cao, et al.
Frontiers in Energy Research (2024) Vol. 11
Open Access | Times Cited: 2
Deep Learning-Powered Intrusion Detection Systems: Enhancing Efficiency in Network Security
M. Balamurugan, UshaBala Varanasi, R. Alarmelu Mangai, et al.
2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (2024), pp. 1-7
Closed Access | Times Cited: 2
A privacy-preserving heterogeneous federated learning framework with class imbalance learning for electricity theft detection
Hanguan Wen, Xiufeng Liu, Bo Lei, et al.
Applied Energy (2024) Vol. 378, pp. 124789-124789
Open Access | Times Cited: 1
An enhanced semi-supervised learning method with self-supervised and adaptive threshold for fault detection and classification in urban power grids
Jiahao Zhang, Lan Cheng, Zhile Yang, et al.
Energy and AI (2024) Vol. 17, pp. 100377-100377
Open Access
A Review on Advancing Cybersecurity Frameworks by Integrating Machine Intelligence with Federated Learning
Vaibhavi Patil, Jhankar Moolchandani, Ashutosh Kumar Dubey
(2024), pp. 180-187
Closed Access
Misbehaviour Detection for Smart Grids using a Privacy-centric and Computationally Efficient Federated Learning Approach
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, et al.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference (2023), pp. 2269-2274
Closed Access
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