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 state-of-the-art survey on solving non-IID data in Federated Learning
Xiaodong Ma, Jia Zhu, Zhihao Lin, et al.
Future Generation Computer Systems (2022) Vol. 135, pp. 244-258
Closed Access | Times Cited: 193

Showing 26-50 of 193 citing articles:

FedHKD: A Hierarchical Federated Learning Approach Integrating Clustering and Knowledge Distillation for Non-IID Data
S. Hu, Changji Wang, Yuan Li, et al.
Lecture notes in computer science (2025), pp. 105-116
Closed Access

GridFL: A 3D-Grid-based Federated Learning framework
Jiagao Wu, Youyi Jiang, Z. Fan, et al.
Journal of Network and Computer Applications (2025), pp. 104115-104115
Closed Access

Enhancing federated averaging of self-supervised monocular depth estimators for autonomous vehicles with Bayesian optimization
Elton Soares, Emílio Vital Brazil, Carlos Alberto V. Campos
Future Generation Computer Systems (2025), pp. 107752-107752
Closed Access

FedADDP: Privacy-Preserving Personalized Federated Learning with Adaptive Dimensional Differential Privacy
Yangyang Guo, Tao Zhang, Xutong Mu, et al.
Lecture notes in computer science (2025), pp. 22-40
Closed Access

CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
Feng Shu, Lin Gao, Leyi Shi
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2416-2416
Open Access

A self-organized MoE framework for distributed federated learning
Jungjae Lee, Wooseong Kim
Future Generation Computer Systems (2025), pp. 107798-107798
Closed Access

Confidence Ensembles: Tabular Data Classifiers on Steroids
Tommaso Zoppi, Peter Popov
Information Fusion (2025), pp. 103126-103126
Open Access

Federated Learning for Scalable Video Streaming
Mahmoud Darwich, Magdy Bayoumi
(2025), pp. 61-91
Closed Access

Heterogeneous Federated Learning with Controlled Gradient Variate of Client Momentum
Rui Zhao, Xiao Yang, X.R. Duan, et al.
Lecture notes in computer science (2025), pp. 179-191
Closed Access

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions
Md Palash Uddin, Yong Xiang, Mahmudul Hasan, et al.
ACM Computing Surveys (2025)
Closed Access

Self-supervised learning of monocular depth estimators in autonomous vehicles with federated learning
Elton Soares, Carlos Alberto V. Campos
Engineering Applications of Artificial Intelligence (2025) Vol. 151, pp. 110572-110572
Closed Access

f-FNC: Privacy concerned efficient federated approach for fake news classification
Vikas Khullar, Harjit Singh
Information Sciences (2023) Vol. 639, pp. 119017-119017
Closed Access | Times Cited: 12

Enhancing federated learning robustness in adversarial environment through clustering Non-IID features
Yanli Li, Dong Yuan, Abubakar Sadiq Sani, et al.
Computers & Security (2023) Vol. 132, pp. 103319-103319
Open Access | Times Cited: 12

Decentralized Machine Learning Training: A Survey on Synchronization, Consolidation, and Topologies
Qazi Waqas Khan, Anam Nawaz Khan, Atif Rizwan, et al.
IEEE Access (2023) Vol. 11, pp. 68031-68050
Open Access | Times Cited: 11

Limitations and Future Aspects of Communication Costs in Federated Learning: A Survey
Muhammad Asad, Saima Shaukat, Dou Hu, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7358-7358
Open Access | Times Cited: 11

FedAEB: Deep Reinforcement Learning Based Joint Client Selection and Resource Allocation Strategy for Heterogeneous Federated Learning
Feng Zheng, Yuze Sun, Bin Ni
IEEE Transactions on Vehicular Technology (2024) Vol. 73, Iss. 6, pp. 8835-8846
Closed Access | Times Cited: 4

Horizontal Federated Recommender System: A Survey
Lingyun Wang, Hanlin Zhou, Yinwei Bao, et al.
ACM Computing Surveys (2024) Vol. 56, Iss. 9, pp. 1-42
Open Access | Times Cited: 4

Concurrent vertical and horizontal federated learning with fuzzy cognitive maps
José L. Salmerón, Irina Arévalo
Future Generation Computer Systems (2024) Vol. 162, pp. 107482-107482
Open Access | Times Cited: 4

Recent Advancements in Federated Learning: State of the Art, Fundamentals, Principles, IoT Applications and Future Trends
Christos Papadopoulos, Konstantinos-Filippos Kollias, George F. Fragulis
Future Internet (2024) Vol. 16, Iss. 11, pp. 415-415
Open Access | Times Cited: 4

Federated learning design and functional models: survey
A John, Sapdo Utomo, Adarsh Rouniyar, et al.
Artificial Intelligence Review (2024) Vol. 58, Iss. 1
Open Access | Times Cited: 4

Federated learning with complete service commitment of data heterogeneity
Y.-H. Zhou, Junxiao Wang, Qin Yuchen, et al.
Knowledge-Based Systems (2025), pp. 112937-112937
Closed Access

Distributed training of foundation models for ophthalmic diagnosis
Sina Gholami, Fatema-E- Jannat, Atalie C. Thompson, et al.
Communications Engineering (2025) Vol. 4, Iss. 1
Open Access

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