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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:

A Secure Federated Learning Framework for Residential Short-Term Load Forecasting
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, et al.
IEEE Transactions on Smart Grid (2023) Vol. 15, Iss. 2, pp. 2044-2055
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Enhancing smart grid load forecasting: An attention-based deep learning model integrated with federated learning and XAI for security and interpretability
Md Al Amin Sarker, Bharanidharan Shanmugam, Sami Azam, et al.
Intelligent Systems with Applications (2024) Vol. 23, pp. 200422-200422
Open Access | Times Cited: 8
Exploring the role of energy Communities: A comprehensive review
Muhammad Amin, Renato Procopio, Marco Invernizzi, et al.
Energy Conversion and Management X (2025), pp. 100883-100883
Open Access
Personalized federated learning for household electricity load prediction with imbalanced historical data
Shibo Zhu, Xiaodan Shi, Huan Zhao, et al.
Applied Energy (2025) Vol. 384, pp. 125419-125419
Open Access
Personalized Federated Learning for Household Energy Demand Prediction Using Model-Based Clustering
Óscar Cabrera Redondo, Sara Barja-Martinez, Mònica Aragüés‐Peñalba
(2025)
Closed Access
Similarity-driven truncated aggregation framework for privacy-preserving short term load forecasting
Ahsan Raza Khan, Mohammad Al-Quraan, Lina Mohjazi, et al.
Internet of Things (2025), pp. 101530-101530
Open Access
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
Mutual Knowledge Distillation Based Federated Learning for Short-term Forecasting in Electric IoT Systems
Cheng Tong, Linghua Zhang, Yin Ding, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 19, pp. 31190-31205
Closed Access | Times Cited: 3
Energy-Aware Federated Learning for AQI Pollutants Forecasting in Edge Networks
C. Venkatesan, S Jeevanantham, B. Rebekka
IEEE Transactions on Network Science and Engineering (2024) Vol. 11, Iss. 5, pp. 4146-4157
Closed Access | Times Cited: 2
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets
Chenghao Huang, Shengrong Bu, Weilong Chen, et al.
IEEE Transactions on Network Science and Engineering (2024) Vol. 11, Iss. 5, pp. 5073-5086
Closed 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
PPFGED: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings
Rong Xie, Zhong Chen, Congying Wu, et al.
Expert Systems with Applications (2023) Vol. 243, pp. 122758-122758
Closed Access | Times Cited: 5
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, et al.
IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (2023), pp. 1-6
Open Access | Times Cited: 4
Task-Aware Machine Unlearning and Its Application in Load Forecasting
Wangkun Xu, Fei Teng
IEEE Transactions on Power Systems (2024) Vol. 39, Iss. 6, pp. 7178-7189
Open Access | Times Cited: 1
CFL-ICCV: Clustered federated learning framework with an intra-cluster cross-validation mechanism for DER forecasting
Linbin Liu, June Li, Juan Wang
Applied Energy (2024) Vol. 377, pp. 124699-124699
Closed Access | Times Cited: 1
FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid
Harshit Gupta, Piyush Agarwal, Kartik Gupta, et al.
Energies (2023) Vol. 16, Iss. 24, pp. 8097-8097
Open Access | Times Cited: 4
A Comparative Analysis of Time Series and Machine Learning Models for Wind Speed Prediction
Rakesh Kumar, M. Prakash, B. Shakila
(2023), pp. 1-6
Closed Access | Times Cited: 3
Cosine-Similarity Truncated Aggregation (Csta): A Hybrid Approach for Federated Stlf
Ahsan Raza Khan, Mohammad Al-Quraan, Lina Mohjazi, et al.
(2024)
Closed Access
Adversarial Reinforcement Learning Against Statistic Inference on Agent Identity
Yue Tian, Qi Jiang, Zuxing Li, et al.
IEEE Access (2024) Vol. 12, pp. 70305-70317
Open Access
Privacy Leakage in Federated Home Applications Using Gradient Inversion Algorithms
Wenzhi Chen, Hongjian Sun, Minglei You, et al.
2022 IEEE International Conference on Industrial Technology (ICIT) (2024) Vol. 1, pp. 1-6
Closed Access
Personalized Federated Learning for Household Electricity Load Prediction with Imbalanced Historical Data
Shibo Zhu, Xiaodan Shi, Huan Zhao, et al.
(2024)
Closed Access
Federated learning assisted distributed energy optimization
Yuhan Du, Nuno Mendes, Simin Rasouli, et al.
IET Renewable Power Generation (2024)
Open Access
Federated Learning: A Paradigm Shift in Cybersecurity for Smart Grids
Owen O'Connor, Tarek Elfouly
(2024), pp. 821-824
Closed Access
Blockchain-Enabled Federated Transfer Learning for Anomaly Detection of Power Lines
Tianjing Wang, Zhao Yang Dong, Lingzhi Su
2021 IEEE Power & Energy Society General Meeting (PESGM) (2024), pp. 1-5
Closed Access
A Load Forecasting Model for Smart Grid Based on RF-BiLSTM and Federated Learning
Wenhui Li, Huilin Jiang, Lei Zhang
(2024)
Closed Access
Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting
Hexiao Li, Sixing Wu, Ruiqi Wang, et al.
IET Generation Transmission & Distribution (2023) Vol. 17, Iss. 22, pp. 5090-5100
Open Access | Times Cited: 1
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