The DOI matches its Crossref record; 48 of the 49 references it cites were found in a registry or an index.
Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Open accessAbstract
Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geographical locations, and learnt one model for each community. Throughout the learning process, the data was kept local at hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline federated machine learning (FL) algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Federated learning is a viable method to connect EHR data from medical institutions, allowing them to share their experiences, and not their data, with a guarantee of privacy [9, 25, 34, 45, 65, 82].”
From Federated Learning for Healthcare Informatics (2021) · cites this paper as [45]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“In the context of electronic health records (EHR), for example, FL helps to represent and to find clinically similar patients13,47, as well as predicting hospitalisations due to cardiac events14, mortality and ICU stay time19.”
From The future of digital health with federated learning (2020) · cites this paper as [19]
In the citing paper
“For example, with access to electronic health records, FL can help to find clinically similar patients and predict hospitalizations due to cardiac events, mortality and ICU stay time [52].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [52]
In the citing paper
“FL is a highly effective method to gather EHR data from hospitals to provide a knowledge-sharing platform without the need of sharing personal data, all while preserving the privacy [104, 105].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [105]
In the citing paper
“Moreover, to support the federated mortality prediction, patient clustering is adopted in [86], where patient subgroups capture similar diagnoses and geographical locations to train a neural network model to forecast mortality based on drug features.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [86]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076
Improving Primary Care for Patients With Chronic Illness
Bodenheimer, T. (2002) · JAMA · doi:10.1001/jama.288.14.1775 · Cited by 2,961
Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv · doi:10.48550/arxiv.1511.06335 · Cited by 2,476
Improving Primary Care for Patients With Chronic Illness: The Chronic Care Model, Part 2
Bodenheimer, T., Wagner, E. H., & Grumbach, K. (2002) · JAMA · doi:10.1001/jama.288.15.1909 · Cited by 2,413
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
Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993
Flagged first, then by citations
1 of the 49 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–200 of 435
Fan, G., Yang, S., Liu, H., Xu, N., Chen, Y., He, J., Su, X., Pang, M., Liu, B., Han, L., et al. (2021) · Spine · doi:10.1097/brs.0000000000004267 · Cited by 41
Li, S., Liu, P., Nascimento, G. G., Wang, X., Leite, F. R. M., Chakraborty, B., Hong, C., Ning, Y., Xie, F., Teo, Z. L., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad170 · Cited by 53
Federated Multi-view Learning for Private Medical Data Integration and Analysis
Che, S., Kong, Z., Peng, H. M., Sun, L., Leow, A., Chen, Y., & He, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501816 · Cited by 45
FedFV: A Personalized Federated Learning Framework for Finger Vein Authentication
Lian, F., Huang, J., Liu, J., Chen, G., Zhao, J., & Kang, W. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1341-4 · Cited by 41
Comparative visual analytics for assessing medical records with sequence embedding
Guo, R., Fujiwara, T., Li, Y., Lima, K. M., Sen, S., Tran, N. K., & Ma, K. (2020) · Visual Informatics · doi:10.1016/j.visinf.2020.04.001 · Cited by 43
A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 46
Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions
Tu, J., Yang, L., & Cao, J. (2024) · ACM Computing Surveys · doi:10.1145/3708495 · Cited by 48
Tuladhar, A., Gill, S., Ismail, Z., & Forkert, N. D. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103424 · Cited by 39
Blockchain-Based Federated Learning in Medicine
El-Rifai, O., Biotteau, M., Boissezon, X. D., Megdiche, I., Ravat, F., & Teste, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_20 · Cited by 40
Detecting Electrocardiogram Arrhythmia Empowered With Weighted Federated Learning
Asif, R. N., Ditta, A., Alquhayz, H., Abbas, S., Khan, M. A., Ghazal, T. M., & Lee, S.-W. (2023) · IEEE Access · doi:10.1109/access.2023.3347610 · Cited by 37
Federated Learning for Arrhythmia Detection of Non-IID ECG
Zhang, M., Wang, Y., & Luo, T. (2020) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc51575.2020.9344971 · Cited by 37
Knowledge Graph Embeddings for ICU readmission prediction
Carvalho, R. M. S., Oliveira, D., & Pesquita, C. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02070-7 · Cited by 38
Security, privacy, and robustness for trustworthy AI systems: A review
Saeed, M. M., & Alsharidah, M. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109643 · Cited by 45
Federated Learning in Smart Healthcare: A Survey of Applications, Challenges, and Future Directions
Nasajpour, M., Pouriyeh, S., Parizi, R. M., Han, M., Mosaiyebzadeh, F., Liu, L., Xie, Y., & Batista, D. M. (2025) · Electronics · doi:10.3390/electronics14091750 · Cited by 48
[Retracted] Federated Deep Learning Approaches for the Privacy and Security of IoT Systems
Alazzam, M. B., Alassery, F., & Almulihi, A. H. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1522179 · Cited by 36
RetractedZhao, H., Sui, D., Wang, Y., Ma, L., & Wang, L. (2025) · Sensors · doi:10.3390/s25082374 · Cited by 39
Aljohani, A. A. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00568-8 · Cited by 45
Ribeiro, M. H. D. M., Mariani, V. C., & Coelho, L. D. S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103575 · Cited by 35
Biodiagnostics in an era of global pandemics—From biosensing materials to data management
Broza, Y. Y., & Haick, H. (2021) · Open Research (University of Surrey) · doi:10.1002/viw.20200164 · Cited by 33
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Lu, J., Zhang, X. S., Zhao, T., He, X., & Cheng, J. (2022) · 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/cvpr52688.2022.00981 · Cited by 38
Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis
Saturi, S. (2022) · Regenerative Engineering and Translational Medicine · doi:10.1007/s40883-022-00273-y · Cited by 37
Shah, S. T., Ali, Z., Waqar, M., & Kim, A. (2025) · Healthcare · doi:10.3390/healthcare13212760 · Cited by 41
Multi-Agent Visualization for Explaining Federated Learning
Wei, X., Li, Q., Liu, Y., Yu, H., Chen, T., & Yang, Q. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/960 · Cited by 33
Auxiliary Diagnosis of COVID-19 Based on 5G-Enabled Federated Learning
Wang, R., Xu, J., Ma, Y., Talha, M., Al-Rakhami, M. S., & Ghoneim, A. (2021) · IEEE Network · doi:10.1109/mnet.011.2000704 · Cited by 33
FLRA: A Reference Architecture for Federated Learning Systems
Lo, S. K., Lu, Q., Paik, H.-Y., & Zhu, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86044-8_6 · Cited by 33
The pneumonia severity index: Assessment and comparison to popular machine learning classifiers
Wang, D., Willis, D. R., & Yih, Y. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104778 · Cited by 33
Blockchain and Machine Learning in EHR Security: A Systematic Review
Zukaib, U., Cui, X., Hassan, M., Harris, S., Hadi, H. J., & Zheng, C. (2023) · IEEE Access · doi:10.1109/access.2023.3333229 · Cited by 36
A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of ThingsPreprint
Zhou, J., Zhang, S., Lu, Q., Dai, W. W., Chen, M., Liu, X., Pirttikangas, S., Shi, Y., Zhang, W., & Herrera‐Viedma, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.10501 · Cited by 65
Selecting Privacy-Enhancing Technologies for Managing Health Data Use
Jordan, S. R., Fontaine, C., & Hendricks‐Sturrup, R. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.814163 · Cited by 35
DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT
Samikwa, E., Maio, A. D., & Braun, T. I. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3313514 · Cited by 32
A spatial–temporal model for network-wide flight delay prediction based on federated learning
Shen, X., Chen, J., & Yan, R. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111380 · Cited by 30
Algorithm Fairness in AI for Medicine and HealthcarePreprint
Chen, R. J., Chen, T., Lipková, J., Wang, J. J., Williamson, D. F. K., Lu, M. Y., Sahai, S., & Mahmood, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.00603 · Cited by 31
Survey of Medical Applications of Federated Learning
Choi, G., Cha, W. C., Lee, S. U., & Shin, S.-Y. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.1.3 · Cited by 33
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
Kong, F., Wang, X., Xiang, J., Yang, S., Wang, X., Yue, M., Zhang, J., Zhao, J., Han, X., Dong, Y., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.03.028 · Cited by 36
CVC: A Collaborative Video Caching Framework Based on Federated Learning at the Edge
Li, Y., Hu, S., & Li, G. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3135306 · Cited by 29
Alabdulhafith, M., Saleh, H., Elmannai, H., Ali, Z. H., El-Sappagh, S., Hu, J.-W., & El-Rashidy, N. (2023) · IEEE Access · doi:10.1109/access.2023.3312343 · Cited by 27
Naresh, V. S., & Reddi, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01098-6 · Cited by 31
Gahlan, N., & Sethia, D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19467-3 · Cited by 34
Inspecting the Running Process of Horizontal Federated Learning via Visual Analytics
Li, Q., Wei, X., Lin, H., Liu, Y., Chen, T., & Ma, X. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3074010 · Cited by 26
Clustering Diagnoses From 58 Million Patient Visits in Finland Between 2015 and 2018
Fränti, P., Sieranoja, S., Wikström, K., & Laatikainen, T. K. (2022) · JMIR Medical Informatics · doi:10.2196/35422 · Cited by 26
El-Rashidy, N. M., Sedik, A., Siam, A. I., & Ali, Z. H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08258-w · Cited by 27
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
Liu, Y., Zhang, L., Ge, N., & Li, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01973 · Cited by 45
Personalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU
Deng, T., Hamdan, H., Yaakob, R., & Kasmiran, K. A. (2023) · IEEE Access · doi:10.1109/access.2023.3241488 · Cited by 26
FedBranched: Leveraging Federated Learning for Anomaly-Aware Load Forecasting in Energy Networks
Manzoor, H. U., Khan, A. R., Flynn, D., Alam, M. M., Akram, M., Imran, M. A., & Zoha, A. (2023) · Sensors · doi:10.3390/s23073570 · Cited by 24
Federated learning for Healthcare 5.0: a comprehensive survey, taxonomy, challenges, and solutions
Amin, M., Ahmad, S., & Loh, W.-K. (2025) · Soft Computing · doi:10.1007/s00500-025-10508-z · Cited by 29
Building ICU In-hospital Mortality Prediction Model with Federated Learning
Dang, T. K., Tan, K. C., Choo, M., Lim, N., Weng, J., & Feng, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63076-8_18 · Cited by 23
Purohit, R., Verma, J. P. V., Jain, R., & Kumar, A. (2024) · Cluster Computing · doi:10.1007/s10586-024-04738-3 · Cited by 25
Selvi, G. C., Murugan, R., Yenduri, G., Gurrammagari, D. R., Bhulakshmi, D., Kandati, D. R., Supriya, Y., Gadekallu, T. R., Rathore, R. S., & Jhaveri, R. H. (2023) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558266152231128060222 · Cited by 22
Federated Learning via Disentangled Information Bottleneck
Uddin, M. P., Xiang, Y., Lu, X., Yearwood, J. L., & Gao, L. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3187962 · Cited by 22
The value of federated learning during and post-COVID-19
Qian, F., & Zhang, J. A. (2021) · International Journal for Quality in Health Care · doi:10.1093/intqhc/mzab010 · Cited by 22
Diba, B. S., Plabon, J. D., Mowla, T. J., Nahar, N., Mistry, D., Sarker, S., Mridha, M. F., & Shin, J. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110515 · Cited by 21
Decentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection
Giuseppi, A., Manfredi, S., Menegatti, D., Poli, C., & Pietrabissa, A. (2022) · IEEE Access · doi:10.1109/access.2022.3202922 · Cited by 23
Federation Opacity and the Promise of Federated Learning in Healthcare
Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 27
VADAF: Visualization for Abnormal Client Detection and Analysis in Federated Learning
Meng, L., Wei, Y., Pan, R., Zhou, S., Zhang, J., & Chen, W. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3426866 · Cited by 25
FedEAN: Entity-Aware Adversarial Negative Sampling for Federated Knowledge Graph Reasoning
Meng, L., Liang, K., Yu, H., Liu, Y., Zhou, S., Liu, M., & Liu, X. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3464516 · Cited by 21
Sana, T. Z., Abdulla, S., Nag, A., Das, A., Hassan, M. M., Fiza, Z. Z., Karim, A., & Kabir, S. R. R. (2025) · IEEE Access · doi:10.1109/access.2025.3605165 · Cited by 25
ARFL: Adaptive and Robust Federated Learning
Uddin, M. P., Xiang, Y., Cai, B., Lu, X., Yearwood, J. L., & Gao, L. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3310248 · Cited by 19
Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 20
Qiu, W., Quan, C., Zhu, L., Yu, Y., Wang, Z., Ma, Y., Sun, M., Chang, Y., Qian, K., Hu, B., et al. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3393557 · Cited by 21
Hu, F., Qiu, S., Yang, X., Wu, C., Nunes, M. B., & Chen, H. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.052570 · Cited by 21
Demo: A Blockchain Based Protocol for Federated Learning
Zhang, Q., Palacharla, P., Sekiya, M., Suga, J., & Katagiri, T. (2020) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp49622.2020.9259388 · Cited by 19
Krishnamoorthy, U., & Balasubramani, S. (2024) · Advanced Theory and Simulations · doi:10.1002/adts.202400479 · Cited by 18
Zhu, H., Bai, J., Li, N., Li, X., Liu, D., Buckeridge, D. L., & Li, Y. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01661-8 · Cited by 29
Self-citationEfficient one-off clustering for personalized federated learning
Liang, T., Yuan, C., Lu, C. Y., Li, Y., Yuan, J., & Yin, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110813 · Cited by 19
Federated clustered multi-domain learning for health monitoring
Jiang, S., Li, Y., Firouzi, F., & Chakrabarty, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-51344-9 · Cited by 20
Federated Momentum Contrastive Clustering
Miao, R., & Koyuncu, E. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3653981 · Cited by 18
Qiu, W., Feng, Y., Li, Y., Chang, Y., Qian, K., Hu, B., Yamamoto, Y., & Schuller, B. W. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3428512 · Cited by 18
Early Detection of Type-2 Diabetes Using Federated Learning
Lincy, M., & Kowshalya, A. M. (2020) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset207644 · Cited by 18
K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm
Giordano, D., Mellia, M., & Cerquitelli, T. (2021) · Electronics · doi:10.3390/electronics10101166 · Cited by 18
Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19
FedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Fu, S., Jia, H., Vassilaki, M., Keloth, V. K., Dang, Y., Zhou, Y., Garg, M., Petersen, R. C., St Sauver, J., Moon, S., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104623 · Cited by 17
Neural network training method for materials science based on multi-source databases
Guo, J., Chen, Z., Liu, Z., Li, X., Xie, Z. Y., Wang, Z., & Wang, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-19426-8 · Cited by 18
Securing Federated Sensitive Topic Classification against Poisoning Attacks
Chu, T., García-Recuero, Á., Iordanou, C., Smaragdakis, G., & Laoutaris, N. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.23112 · Cited by 19
FedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks
Fontenla-Romero, Ó., Guijarro‐Berdiñas, B., Hernández-Pereira, E., & Pérez‐Sánchez, B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.018 · Cited by 17
Ooi, M. P., Sohail, S., Huang, V., Hudson, N., Baughman, M., RANA, O. F., Hinze, A., Chard, K., Chard, R., Foster, I. T., et al. (2023) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2023.10328671 · Cited by 20
AFL-DCS: An asynchronous federated learning framework with dynamic client scheduling
Zhang, R., Luo, W., Luo, Y., Zhang, H., & Wang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107927 · Cited by 17
Decision Trees in Federated Learning: Current State and Future Opportunities
Heiyanthuduwage, S. R., Altas, I., Bewong, M., Islam, M. Z., & Deho, O. B. (2024) · IEEE Access · doi:10.1109/access.2024.3440998 · Cited by 18
A Review and Experimental Evaluation on Split Learning
Hu, Z., Zhou, T., Wu, B., Chen, C., & Wang, Y. (2025) · Future Internet · doi:10.3390/fi17020087 · Cited by 19
Federated Learning for Privacy-Preserving Data Access
Śmietanka, M., Pithadia, H., & Treleaven, P. C. (2020) · International Journal of Data Science and Big Data Analytics · doi:10.2139/ssrn.3696609 · Cited by 16
FedRFC: Federated Learning with Recursive Fuzzy Clustering for improved non-IID data training
Deng, Y., Wang, A., Zhang, L., Lei, Y., Li, B., & Li, Y. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.049 · Cited by 16
Application of privacy protection technology to healthcare big data
Shin, H., Ryu, K., Kim, J., & Lee, S. (2024) · Digital Health · doi:10.1177/20552076241282242 · Cited by 17
Privacy Preserving Time-Series Forecasting of User Health Data Streams
Imtiaz, S., Horchidan, S.-F., Abbas, Z., Arsalan, M., Chaudhry, H. N., & Vlassov, V. (2020) · 2020 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata50022.2020.9378186 · Cited by 15
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning
Enthoven, D., & Al-Ars, Z. (2022) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms58070.2022.10062088 · Cited by 28
Hasan, N., & Bao, Y. (2021) · Health and Technology · doi:10.1007/s12553-021-00538-6 · Cited by 18
A Superquantile Approach to Federated Learning with Heterogeneous Devices
Laguel, Y., Pillutla, K., Malick, J., & Harchaoui, Z. (2021) · Conference on Information Sciences and Systems · doi:10.1109/ciss50987.2021.9400318 · Cited by 16
Dew Intelligence: Federated learning perspective
Guberović, E., Lipić, T., & Čavrak, I. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00274 · Cited by 15
Device Heterogeneity in Federated Learning: A Superquantile Approach
Laguel, Y., Pillutla, K., Malick, J., & Harchaoui, Z. (2020) · arXiv · doi:10.48550/arxiv.2002.11223 · Cited by 15
Fedns: Improving Federated Learning for Collaborative Image Classification on Mobile Clients
Zhuo, Y., & Li, B. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428075 · Cited by 15
Federated Self-training for Semi-supervised Audio Recognition
Tsouvalas, V., Saeed, A., & Özçelebi, T. (2022) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3520128 · Cited by 18
Federated learning with superquantile aggregation for heterogeneous data
Pillutla, K., Laguel, Y., Malick, J., & Harchaoui, Z. (2023) · Machine Learning · doi:10.1007/s10994-023-06332-x · Cited by 18
Ahmed, O. S., Omer, E., Alshawwa, S. Z., Alazzam, M. B., & Khan, R. A. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/1201339 · Cited by 13
RetractedNever Too Late: Tracing and Mitigating Backdoor Attacks in Federated Learning
Zeng, H. Y., Zhou, T., Wu, X., & Cai, Z. (2022) · IEEE Symposium on Reliable Distributed Systems (SRDS) · doi:10.1109/srds55811.2022.00017 · Cited by 14
Federated AI, Current State, and Future Potential
Clark, P., Oermann, E. K., Chen, D. K., & Al‐Aswad, L. A. (2023) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000614 · Cited by 13
FedSmart: An Auto Updating Federated Learning Optimization Mechanism
He, A., Wang, J., Huang, Z., & Xiao, J. Z. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60259-8_52 · Cited by 13
Concepts, Key Challenges and Open Problems of Federated Learning
Iqbal, Z., & Chan, H. Y. (2021) · International Journal of Engineering · doi:10.5829/ije.2021.34.07a.11 · Cited by 14
A systematic review on privacy-preserving distributed data mining
Sun, C., Ippel, L., Dekker, A. L. A. J., Dumontier, M., & van Soest, J. (2021) · Data Science · doi:10.3233/ds-210036 · Cited by 12
Security and Privacy Concerns for Healthcare Wearable Devices and Emerging Alternative Approaches
Boumpa, E., Tsoukas, V., Gkogkidis, A., Spathoulas, G. P., & Kakarountas, A. P. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06368-8_2 · Cited by 16
Islam, H., Mosa, A. S. M., & Famia (2021) · PubMed Central · Cited by 13
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge ComputingPreprint
Mills, J., Hu, J. W., & Min, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09236 · Cited by 13
Cowley, H., Robinette, M. S., Matelsky, J., Xenes, D., Kashyap, A., Ibrahim, N. F., Robinson, M. L., Zeger, S. L., Garibaldi, B. T., & Gray-Roncal, W. (2023) · Scientific Reports · doi:10.1038/s41598-022-26294-9 · Cited by 12
Metadata
- Authors
- Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Beijing University of Technology (CN)
- Northeastern University (US)
- Boston Children's Hospital (US)
- Corresponding author
- Liu, Dianbo
- ORCID
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 99
- Pages
- 103291
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2977072935
- Cited by
- 437
- References
- 49
- Citations by year
- 2026: 31 · 2025: 58 · 2024: 87 · 2023: 95 · 2022: 66 · 2021: 60 · 2020: 35 · 2019: 4
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- federated learning, patient clustering, non-IID data, electronic health records, intensive care unit, mortality prediction, length of hospital stay, privacy-preserving machine learning, communication overhead
- MeSH terms
- Machine Learning, Algorithms, Female, Humans, Length of Stay, Male, Middle Aged, Cluster Analysis, Critical Illness, Electronic Health Records
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
