The DOI matches its Crossref record; 16 of the 20 references it cites were found in a registry or an index.
Experiments of Federated Learning for COVID-19 Chest X-ray Images
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In the citing paper
“Similarly, Zhang et al. [24] and Yan et al. [29] used X-ray and CT image data for different Convolutional Neural Network (CNN) architectures in FL settings.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [29]
In the citing paper
“Yan et al. [29] presented their results using sensitivity, their highest score was 91.26%.”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [29]
In the citing paper
“FL for COVID-19 detection model using”
From A systematic review on federated learning in medical image analysis (2023) · cites this paper as [29]
In the citing paper
“The experimental results show that ResNet-18 has the best performance in training both with FL and without FL approaches, while ResNeXt shows the highest efficiency on images with COVID-19 labels.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [49]
Citations
Cites
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639
Wang, L., Lin, Z. Q., & Wong, A. M. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,228
Measure Contribution of Participants in Federated Learning
Wang, G., Dang, C. X., & Zhou, Z. Y. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006179 · Cited by 243
Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242
Self-citationLearning Private Neural Language Modeling with Attentive Aggregation
Ji, S., Pan, S., Long, G., Li, X., Jiang, J., & Huang, Z. H. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852464 · Cited by 172
Human Activity Recognition Using Federated Learning
Sozinov, K., Vlassov, V., & Girdzijauskas, S. (2018) · 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom) · doi:10.1109/bdcloud.2018.00164 · Cited by 167
Learning Private Neural Language Modeling with Attentive Aggregation
Ji, S., Pan, S., Long, G., Li, X., Jiang, J. B., & Huang, Z. H. (2018) · UTS ePRESS (University of Technology Sydney) · Cited by 106
Flagged first, then by citations
4 of the 20 references couldn’t be matched to a known paper.
Research works printed but not linked: 9
- Ayyachamy, S., Alex, V., Khened, M., Krishnamurthi, G.: Medical image retrieval using Resnet-18. In: Medical Imaging 2019, Imaging Informatics for Healthcare, Research, and Applications. vol. 10954, p. 1095410. Proceedings of SPIE (2019)
- Chen, F., Luo, M., Dong, Z., Li, Z., He, X.: Federated meta-learning with fast convergence and efficient communication. arXiv preprint arXiv:1802.07876 (2018) · arXiv
- Chen, M., Sun, Y., Cai, X., Liu, B., Ren, T.: Design and implementation of a novel precision irrigation robot based on an intelligent path planning algorithm. arXiv preprint arXiv:2003.00676 (2020) · arXiv
- COVID-19 image data collection. arXiv 2003.11597. https://github.com/ieee8023/covid-chestxray-dataset. Accessed 04 Dec 2020 · arXiv
- Geyer, R.C., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. arXiv preprint arXiv:1712.07557 (2017) · arXiv
- Konečný, J., et al.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016) · arXiv
- Smith, V., Chiang, C.K., Sanjabi, M., Talwalkar, A.S.: Federated multi-task learning. In: Advances in Neural Information Processing Systems 2017, vol. 30, pp. 4424–4434. Advances in Neural Information Processing Systems, Long Beach, CA (2017)
- Xu, Z., Yang, Z., Xiong, J., Yang, J., Chen, X.: Elfish: resource-aware federated learning on heterogeneous edge devices. arXiv preprint arXiv:1912.01684 (2019) · arXiv
- Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data. arXiv preprint arXiv:1806.00582 (2018) · arXiv
Cited by
Sorted by citations
Items 101–121 of 121
Joint Provisioning of QoS and Privacy with Federated Learning
Fadlullah, Z. M., & Fouda, M. M. (2022) · Springer eBooks · doi:10.1007/978-3-031-04321-5_14 · Cited by 1
Isiaka, R. M., Babatunde, R. S., Ajao, J. F., Yusuff, S. R., Popoola, D. D., AROWOLO, M. O., & Adewole, K. S. (2022) · Advances in Multidisciplinary & Scientific Research Journal Publication · doi:10.22624/aims/maths/v10n4p2 · Cited by 1
Artificial Cognitive Computing for Smart Communications, 5G and Beyond
Amsini, Rai, A., & Rai, A. (2023) · Transactions on computer systems and networks · doi:10.1007/978-981-99-0109-8_1 · Cited by 1
Utilizing Federated Learning for Accurate Prediction of Covid-19 from CT Scan Images
Patel, J., Patel, S., Thakkar, S., & Saraswat, D. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10134193 · Cited by 1
Erasing memories: implementing client unlearning in medical image analysis
Ge, L. (2024) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1117/12.3035404 · Cited by 1
Federated Learning Methodology for Indoor Location Systems in Multi-Layer Architecture
Ruiz, J. L. L., Espinosa, Á. V., & Espinilla, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651450 · Cited by 1
Sharma, V., Challa, V. K. R., Pranavi, P., & Singh, R. P. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2024.12.033 · Cited by 2
Patel, S., & Khan, N. R. (2022) · IEEE International Conference on Current Development in Engineering and Technology (CCET) · doi:10.1109/ccet56606.2022.10080567 · Cited by 1
Retracted: Monitoring Novel Corona Virus using Machine Learning
Mohan, M., Agrawal, V., Malik, M., & Atul, A. (2022) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict54344.2022.9850795
RetractedQahmash, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4193-1_47
COVID-19 Detection Using CNN-ResNet-50 Model
Yashwaanth, S. V., Kadhir, G., Pranadarth, S., Vinoth Raj, R., & Martin, B. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-6068-0_35
Classifying Chest X-Ray Images using Residual Neural Network
Mirandilla, J. P. C., Sybingco, E., Jose, J. A. C., & Dadios, E. P. (2022) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem57413.2022.10109435
The Investigation Related to Application of Federated Learning’s in Chest X-ray Detection
Tan, H. (2024) · Highlights in Science Engineering and Technology · doi:10.54097/0vs6f005
Kumar, B., Evin, M., Byeon, H., Sivaprakash, M., Sheeba, P. M., & Sunil, J. (2024) · IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) · doi:10.1109/incos59338.2024.10527505
Diagnosing coronaviruses (COVID-19) using machine learning
Haque, S. B. U., Zafar, A., Shibli, A. R., & Gourinath, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95374-0.00011-7
Yang, J., Ou, Y., Chen, Z., Zeng, X., Liao, J., Bi, X., Liu, L., Wang, J., Wang, X., Fu, X., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5217803
Self-citationMedical Data Privacy Using Federated Learning: Society 5.0
Ajmi, I., & Das, A. (2025) · Springer tracts in human-centered computing · doi:10.1007/978-981-96-1761-6_36
Federated Learning in the Healthcare Industry
Mondal, R. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-99270-4_2
Li, N., 战星辰, Li, Y., Li, M., & Zhu, D. (2026) · IEEE International Conference on Joint Cloud Computing (JCC) · doi:10.1109/jcc72984.2026.00044
Didi, Y., Walha, A., & Wali, A. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12457-6
Didi, Y., Walha, A., & Wali, A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21915-1
Metadata
- Authors
- Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B.
- Affiliations
- Hainan University (CN)
- University of Chinese Academy of Sciences (CN)
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 41-53
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W3183916646
- Cited by
- 122
- References
- 20
- Citations by year
- 2026: 4 · 2025: 16 · 2024: 20 · 2023: 25 · 2022: 33 · 2021: 21
- FWCI
- 31.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Communications in computer and information science (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- federated learning, COVID-19 detection, chest X-ray images, privacy-preserving machine learning, data silos, health data sharing, MobileNetV2, ResNet-18, ResNeXt, COVID-NET, model performance comparison
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