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
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Liu, B., Wang, L., Liu, M., & Xu, C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2976321 · Cited by 83
Self-citationYan, B., Tang, X., Wang, J., Zhou, Y., Zheng, G., Zou, Q., Lu, Y., Liu, B., Tu, W., & Xiong, N. N. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.011317 · Cited by 41
Self-citationMedical image retrieval using Resnet-18 for clinical diagnosis
Alex, V., Khened, M., Ayyachamy, S., & Krishnamurthi, G. (2019) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.2515588 · Cited by 29
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
Zhang, Y., Chen, J., YangError, Y., Li, H., Zheng, X., Chen, X., Liu, B., Ren, T., & Xiong, N. N. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.011316 · Cited by 27
Self-citationSpatial Image Steganalysis Based on ResNeXt
Sharma, A., & Muttoo, S. K. (2018) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct.2018.8600132 · Cited by 16
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word MiningPreprint
Zhang, Y., Chen, J., Liu, B., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.11458 · Cited by 6
Self-citation
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
Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889
Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System
Zhang, L., Xu, J., Vijayakumar, P., Sharma, P. K., & Ghosh, U. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3185327 · Cited by 376
Federated Learning in Edge Computing: A Systematic Survey
Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022) · Sensors · doi:10.3390/s22020450 · Cited by 384
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Dynamic-Fusion-Based Federated Learning for COVID-19 Detection
Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056185 · Cited by 329
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
