A distribution information sharing federated learning approach for medical image data
Open accessAbstract
In recent years, federated learning has been believed to play a considerable role in cross-silo scenarios (e.g., medical institutions) due to its privacy-preserving properties. However, the non-IID problem in federated learning between medical institutions is common, which degrades the performance of traditional federated learning algorithms. To overcome the performance degradation problem, a novelty distribution information sharing federated learning approach (FedDIS) to medical image classification is proposed that reduce non-IIDness across clients by generating data locally at each client with shared medical image data distribution from others while protecting patient privacy. First, a variational autoencoder (VAE) is federally trained, of which the encoder is uesd to map the local original medical images into a hidden space, and the distribution information of the mapped data in the hidden space is estimated and then shared among the clients. Second, the clients augment a new set of image data based on the received distribution information with the decoder of VAE. Finally, the clients use the local dataset along with the augmented dataset to train the final classification model in a federated learning manner. Experiments on the diagnosis task of Alzheimer’s disease MRI dataset and the MNIST data classification task show that the proposed method can significantly improve the performance of federated learning under non-IID cases.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy.
“In addition, federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy [152-156].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [152]
The Introduction (page not provided) says federated learning can train a model across medical institutions without local data leaving them, addressing patient privacy. The paper studies medical images, however, and does not establish the claim about leveraging multimodal health-care data; its Conclusion treats heterogeneous images from different institutions as future work. (LLM, conf=0.87)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322 · see citing works
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data
Sattler, F., Wiedemann, S., Muller, K.-R., & Samek, W. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2944481 · Cited by 1,725 · see citing works
Model-Contrastive Federated Learning
Li, Q., He, B., & Song, D. X. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01057 · Cited by 1,634 · see citing works
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005 · see citing works
Federated Learning with Matched AveragingPreprint
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., & Khazaeni, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.06440 · Cited by 725 · see citing works
On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems
Särkkä, S. (2007) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2007.904453 · Cited by 559 · see citing works
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 · see citing works
Bringing Old Photos Back to Life
Wan, Z., Zhang, B., Chen, D., Zhang, P., Chen, D., Liao, J., & Wen, F. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00282 · Cited by 245 · see citing works
Guo, P., Wang, P., Zhou, J., Jiang, S., & Patel, V. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00245 · Cited by 193 · see citing works
MR Image Reconstruction Using Deep Density Priors
Tezcan, K. C., Baumgartner, C. F., Luechinger, R., Pruessmann, K. P., & Konukoğlu, E. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2887072 · Cited by 167 · see citing works
Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data
Yan, Z., Wicaksana, J., Wang, Z., Yang, X., & Cheng, K.-T. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3040015 · Cited by 137 · see citing works
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
Sun, Y., Zhou, S., & Gündüz, D. (2020) · International Conference on Communications · doi:10.1109/icc40277.2020.9148853 · Cited by 114 · see citing works
Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders
Pesteie, M., Abolmaesumi, P., & Rohling, R. N. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2914656 · Cited by 102 · see citing works
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Zhu, Z., Hong, J., & Zhou, J. (2021) · PubMed · doi:10.48550/arxiv.2105.10056 · Cited by 66 · see citing works
Deep Federated Learning for IoT-based Decentralized Healthcare Systems
Elayan, H., Aloqaily, M., & Guizani, M. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498820 · Cited by 61 · see citing works
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57 · see citing works
Fed-Focal Loss for imbalanced data classification in Federated LearningPreprint
Sarkar, D., Narang, A., & Rai, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06283 · Cited by 34 · see citing works
Target-Independent Domain Adaptation for WBC Classification Using Generative Latent Search
Pandey, P., Prathosh, A. P., Kyatham, V., Mishra, D., & Dastidar, T. R. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3009029 · Cited by 33 · see citing works
Handling autism imbalanced data using synthetic minority over-sampling technique (SMOTE)
El-Sayed, A. A., Mahmood, M. A., Meguid, N. A., et al. (2015) · World Conference on Complex Systems · doi:10.1109/icocs.2015.7483267 · Cited by 25 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [2]Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y (2020) BatchCrypt: Efficient homomorphic encryption for Cross-Silo federated learning. In 2020 USENIX annual technical conference (USENIX ATC 20), pp. 493-506
- [9]Karimireddy SP, Kale S, Mohri M, Reddi S, Stich S, Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning, pp 5132–5143
- [13]Lin T Kong L Stich SU Jaggi M Ensemble distillation for robust model fusion in federated learning Adv Neural Inf Process Syst 2020 33 2351 2363
- [18]Wang L Xu S Wang X Zhu Q Addressing class imbalance in federated learning Proc AAAI Conf Artif Intell 2021 35 11 10165 10173
Cited by
Sorted by citations
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349 · see citing works
“In addition, federated learning presents a viable solution for leveraging multimodal data from multiple health care institutions without the need to share the actual data, thus maintaining patient privacy [152-156].”
- Authors
- Zhao, L., & Huang, J.
- Affiliations
- Shenzhen University (CN)
- Corresponding author
- Huang, Jianjun
- ORCID
- Huang, Jianjun
- Venue
- Complex & Intelligent Systems
- Published
- 2023-03-29
- Volume
- 9
- Issue
- 5
- Pages
- 5625-5636
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- PMC
- PMC10052320
- OpenAlex
- W4361289277
- Cited by
- 23
- References
- 22
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 9 · 2023: 2
- FWCI
- 2.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Science and Technology Planning Project of Shenzhen Municipality (20200821152629001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- federated learning, non-IID data, medical image classification, variational autoencoder, data augmentation, cross-silo federated learning, patient privacy, MNIST
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
