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Federated learning of predictive models from federated Electronic Health Records
Open accessAbstract
Background In an era of "big data," computationally efficient and privacy-aware solutions for large-scale machine learning problems become crucial, especially in the healthcare domain, where large amounts of data are stored in different locations and owned by different entities. Past research has been focused on centralized algorithms, which assume the existence of a central data repository (database) which stores and can process the data from all participants. Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data. Given scores of data widely spread across hospitals/individuals, a decentralized computationally scalable methodology is very much in need.Objective We aim at solving a binary supervised classification problem to predict hospitalizations for cardiac events using a distributed algorithm. We seek to develop a general decentralized optimization framework enabling multiple data holders to collaborate and converge to a common predictive model, without explicitly exchanging raw data.Methods We focus on the soft-margin l 1-regularized sparse Support Vector Machine (sSVM) classifier. We develop an iterative cluster Primal Dual Splitting (cPDS) algorithm for solving the large-scale sSVM problem in a decentralized fashion. Such a distributed learning scheme is relevant for multi-institutional collaborations or peer-to-peer applications, allowing the data holders to collaborate, while keeping every participant's data private.Results We test cPDS on the problem of predicting hospitalizations due to heart diseases within a calendar year based on information in the patients Electronic Health Records prior to that year. cPDS converges faster than centralized methods at the cost of some communication between agents. It also converges faster and with less communication overhead compared to an alternative distributed algorithm. In both cases, it achieves similar prediction accuracy measured by the Area Under the Receiver Operating Characteristic Curve (AUC) of the classifier. We extract important features discovered by the algorithm that are predictive of future hospitalizations, thus providing a way to interpret the classification results and inform prevention efforts.
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“A similar scheme is used by Xie et al. [485] to design a variant of local SGD in which clients locally perform Adagrad [335, 161].”
From Advances and Open Problems in Federated Learning (2021) · cites this paper as [485]
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In the citing paper
“Federated learning has also enabled predictive modeling based on diverse sources, which can provide clinicians with additional insights into the risks and benefits of treating patients earlier [9, 10, 90].”
From Federated Learning for Healthcare Informatics (2021) · cites this paper as [10]
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 [14]
In the citing paper
“Governance of data for FL is maintained locally, alleviating privacy concerns, with only model weights or gradients communicated between the client-sites and the federated server38,39.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [39]
In the citing paper
“For example, FL can be used to improve the perceiving and learning ability of AIoT devices such as connected vehicles for autonomous driving [199] and wearable devices for health monitoring [222], [197].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [222]
In the citing paper
“The FL algorithm is used to describe and identify physician-related persons and to estimate their hospitalization, deaths, and survival rates based on electronic health records (EHR) [54].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [54]
In the citing paper
“Deep Leakage from Gradients (DLG) [152] obtained the private training data at the inference phase.”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [152]
In the citing paper
“The theoretical and experimental comparisons show that the proposed model converges faster and with less communication overhead compared to an alternative distributed algorithm.”
From Handling privacy-sensitive medical data with federated learning: Challenges and future directions (2023) · cites this paper as [35]
In the citing paper
“Furthermore, an FL-based approach is proposed in [81] to predict hospitalizations for patients diagnosed with heart diseases using their historic EHRs.”
From Federated learning for smart healthcare: A survey (2023) · cites this paper as [81]
In the citing paper
“In addition, Brisimi et al., (2018) also use EMRs to evaluate whether a patient with heart disease will be hospitalized based on a FL algorithm called cluster Primal Dual Splitting (cPDS).”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al., (2018)
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
In the citing paper
“Brisimi et al. 2018 Predict future hospitalizations for patients Cluster Primal Dual Splitting algorithm Yield classifiers using relatively few features Need more iterations for convergence”
From A review of applications in federated learning (2020) · cites this paper as Brisimi et al. 2018
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Selvaraj, C., Cho, W. C. S., Kulanthaivel, L., Alothaim, A. S., Vijayakumar, R., Jayaprakashvel, M., & Desai, D. (2025) · 3 Biotech · doi:10.1007/s13205-025-04518-9 · Cited by 9
“This has the potential to reveal hidden biological mechanisms involved in cancer with new therapeutic possibilities (Chen et al. 2018).”
On-Device Federated Learning for Energy-Efficient Smart Irrigation
Dakhia, Z., Lazzaro, A., Sebti, M. R., Russo, M., & Merenda, M. (2025) · Electronics · doi:10.3390/electronics14214311 · Cited by 6
Shiraj, T. B., Jahan, S., Adib, M. R. S., Rahman, M. S., Kaiser, M. S., & Hosen, A. S. M. S. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131514 · Cited by 6
Global approaches to infectious disease surveillance and modeling
Khurana, M. P., Tsui, J. L.-H., Gutierrez, B., Chopra, A., Scheidwasser, N., Zhu, H. B. H., Chang, S. Y., Duchêne, D. A., Mills, C. L., Inward, R. P. D., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04351-4 · Cited by 9
FedAFR: Enhancing Federated Learning with adaptive feature reconstruction
Huang, Y., Zhu, S., Chen, W., & Huang, Z. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.12.007 · Cited by 6
Federated learning in healthcare applications
Kanhegaonkar, P., & Prakash, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-313233-9.00013-8 · Cited by 6
TPE-BFL: Training Parameter Encryption scheme for Blockchain based Federated Learning system
Shen, F., Liang, Q., Hui, L., Yang, B., Xu, C., Feng, J., & He, Y. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110691 · Cited by 6
Optimizing Digital Market Decision-Making Through Artificial Intelligence Platforms
Chen, R., & Chen, Y. (2024) · Journal of Global Information Management · doi:10.4018/jgim.365908 · Cited by 12
Self-citationToward Optimized Federated Learning With Compressed Communications by Rate Adaption
Cui, L., Su, X., Zhou, Y., Liu, J., & Wen, S. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2024.3520530 · Cited by 7
“It effectively utilizes the rich data spread across different geo-locations and organizations, without compromising their privacy [4], e.g. , for smart devices to predict human trajectories [5] or for healthcare systems to develop predictive models [6].”
Cross-subject federated transfer learning with quanvolutional layer for Motor Imagery classification
Hu, R., Zhou, X., Xu, Z., Liao, Z., Wu, H., Qu, H., & Tang, Z. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727351 · Cited by 6
Decentralized Machine Learning for Intelligent Health-Care Systems on the Computing Continuum
Kimovski, D., Ristov, S., & Prodan, R. (2022) · Computer · doi:10.1109/mc.2022.3142151 · Cited by 6
“The current attempts to integrate ML and IoT with EHR systems, such as federated learning, which enables training across multiple geographically devices, already yielded promising results [13].”
BackgroundVerma, A., Bhattacharya, P., Budhiraja, I., Gupta, A. K., & Tanwar, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5037-7_15 · Cited by 6
Health Synthetic Data to Enable Health Learning System and Innovation: A Scoping Review
Tsao, S., Sharma, K., Noor, H., Forster, A. J., & Chen, H. (2023) · Studies in health technology and informatics · doi:10.3233/shti230063 · Cited by 10
“This can prevent critical data compromises, but it has not been optimized and implemented at a larger scale [10-11].”
BackgroundCADUCEO: A Platform to Support Federated Healthcare Facilities through Artificial Intelligence
Menegatti, D., Giuseppi, A., Priscoli, F. D., Pietrabissa, A., Giorgio, A. D., Baldisseri, F., Mattioni, M., Monaco, S., Lanari, L., Panfili, M., et al. (2023) · Healthcare · doi:10.3390/healthcare11152199 · Cited by 6
Long-Tailed Federated Learning Via Aggregated Meta Mapping
Qian, P., Lu, Y., & Wang, H. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10223069 · Cited by 6
Artificial Intelligence in Surgical Research: Accomplishments and Future Directions
Rogers, M. P., Janjua, H. M., Walczak, S. M., Baker, M. S., Read, M. D., Cios, K. J., Velanovich, V., Pietrobon, R. S., & Kuo, P. C. (2023) · The American Journal of Surgery · doi:10.1016/j.amjsurg.2023.10.045 · Cited by 6
Towards a Federated Learning Approach for NLP Applications
Prabhu, O., Gupta, P. K., Shashank, P., Chandrasekaran, K., & Usha, D. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3067-5_13 · Cited by 6
Collaborative analysis of genomic data: vision and challenges
Jafarbeiki, S., Gaire, R., Sakzad, A., Kermanshahi, S. K., & Steinfeld, R. (2021) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic52973.2021.00020 · Cited by 6
Distributionally Robust Federated Learning for Differentially Private Data
Shi, S., Hu, C., Wang, D., Zhu, Y., & Han, Z. (2022) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs54860.2022.00086 · Cited by 6
“Federated learning is becoming an increasingly important paradigm in many data-intensive applications ranging from prevalent personal mobile apps [1] [2] and privacy-intensive verticals, e.g., health, finance [3] [4].”
BackgroundChen, R., Paschalidis, I. C., Hatabu, H., Valtchinov, V. I., & Siegelman, J. (2019) · European Journal of Radiology Open · doi:10.1016/j.ejro.2019.04.007 · Cited by 6
Self-citationCohort-based federated learning services for industrial collaboration on the edgePreprint
Hiessl, T., Lakani, S. R., Kemnitz, J., Schall, D., & Schulte, S. (2021) · J. Parallel Distributed Comput. · doi:10.36227/techrxiv.14852361.v1 · Cited by 30
“Despite being a very recent research topic, FL has been studied widely in various applications [32], [33], [34].”
BackgroundA Survey of Image Gradient Inversion Against Federated LearningPreprint
Li, Z., Wang, L., Chen, G., Shafq, M., & Gu, Z. (2022) · doi:10.36227/techrxiv.18254723 · Cited by 6
“When data cannot be shared due to confidentiality concerns and legal restrictions, federated learning can be used to jointly model data knowledge from various institutions [11], [12].”
Soltan, A. A. S., Thakur, A., Yang, J., Chauhan, A., D'Cruz, L. G., Dickson, P., Soltan, M., THICKETT, D. R., Eyre, D. W., Zhu, T., et al. (2023) · medRxiv · doi:10.1101/2023.05.05.23289554 · Cited by 6
“However, to date real-world implementations of FL in hospital settings have been limited in number 12–16 , and some approaches advocate use of an intermediate platform 13 .”
MethodClinical Research Informatics: Contributions from 2018
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“Another best paper is a paper authored by Brisimi et al., [6] which describes a computationally efficient and privacy-aware solution for large-scale machine learning problems running on distributed data.”
Detection of Malicious Clients in Federated Learning Using Graph Neural Network
Sharma, A., & Marchang, N. (2025) · IEEE Access · doi:10.1109/access.2025.3565712 · Cited by 7
“This decentralized framework addresses critical privacy concerns and is particularly advantageous in sensitive domains such as healthcare [5], [6], [7], [8], [9], [10], [11], [12], [13], finance [14], [15], [16], [17], and edge computing [18], [19], [20], [21], [22], [23], [24].”
Federated graph neural networks in non-IID scenarios—A comprehensive survey
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“The fundamental causes for introducing decentralized method FL in the healthcare sector include data scarcity, data dispersed across multiple organizations, and patient and hospital confidentiality [5].”
BackgroundAdaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling
Geng, J.-X., Hou, Y.-Z., Tao, X.-F., Wang, J.-C., & Luo, B. (2024) · ICC 2024 - IEEE International Conference on Communications · doi:10.1109/icc51166.2024.10622957 · Cited by 5
“FL has broad applications in various fields, spanning computer vision, natural language processing, e-Healthcare, and smart home [3]–[6].”
BackgroundLopez-Martinez, J., Checa, P., Soto-Hidalgo, J. M., Isaac Triguero, A., & Fernández, A. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650749 · Cited by 5
“Finally, ensuring data privacy is paramount in the field of AI, especially when dealing with extensive datasets crucial for training robust learning models in clinical and medical contexts [40].”
BackgroundPrivacy for Free: Spy Attack in Vertical Federated Learning by Both Active and Passive Parties
Fu, C., Chen, H., & Ruan, N. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3534469 · Cited by 8
“F EDERATED learning (FL) [1], [2], [3], [4] has emerged as a new privacy-preserving distributed learning paradigm, as it enables multiple parties to collaboratively train a global model without disclosing raw data.”
Geng, J., Li, B., Qin, X., Li, Y., Li, L., Hou, Y., & Pan, M. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3572516 · Cited by 6
“…the application of FL to mobile devices has ushered in a host of promising possibilities spanning diverse domains including computer vision [3], natural language processing [4] and many other applications such as e-Healthcare [5], voice assistants in smart home [6], and autonomous driving [7].”
Preserving Privacy with Federated Learning in Route Choice Behavior Modeling
Kweon, Y., Sun, B., & Park, B. (2021) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981211011162 · Cited by 5
“However, all of these studies used survey data that is collected from a small group of participants (fewer than 32 participants) and assumed the model developers had access to users’ data To tackle privacy concerns for training models with decentralized data across individual devices, federated learning was first prop…”
BackgroundInfluentialOdeyomi, O. T., & Zaruba, G. V. (2021) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot52608.2021.9454170 · Cited by 5
“Fully decentralized federated learning has been considered in [11], [14]–[17].”
BackgroundMethodPrivacidade do Usuário em Aprendizado Colaborativo: Federated Learning, da Teoria à Prática
Neto, H. C., Mattos, D. M. F., & Fernandes, N. (2020) · doi:10.5753/sbc.8285.6.3 · Cited by 5
Application of Federated Learning Approaches for Time-Series Classification in eHealth Domain
Paragliola, G. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.413 · Cited by 5
BCAFL: A Blockchain-Based Framework for Asynchronous Federated Learning Protection
Yun, J., Lu, Y., & Liu, X. (2023) · Electronics · doi:10.3390/electronics12204214 · Cited by 5
Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning
Jiang, Y., Chen, S., & Bao, X. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3341836 · Cited by 5
Gayathri Hegde, M., Shrishti Bekal, M., Shenoy, P. D., & Venugopal, K. R. (2023) · IEEE Region Humanitarian Technology Conference · doi:10.1109/r10-htc57504.2023.10461823 · Cited by 5
Imboccioli, F., Cialone, G., & Ferretti, S. (2024) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops59983.2024.10502820 · Cited by 5
Metadata
- Authors
- Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W.
- Affiliations
- Boston University (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Paschalidis, Ioannis Ch.
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 112
- Pages
- 59-67
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC5836813
- OpenAlex
- W2783522756
- Cited by
- 997
- References
- 24
- Citations by year
- 2026: 118 · 2025: 159 · 2024: 146 · 2023: 167 · 2022: 156 · 2021: 151 · 2020: 74 · 2019: 20 · 2018: 5
- FWCI
- 35.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (IIS-1724990, CNS-1645681, CCF-1527292, IIS-1237022)
- Boston University
- Army Research Office (W911NF-12-1-0390)
- National Institutes of Health (1UL1TR001430)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Control and Systems Engineering › Distributed Control Multi-Agent Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- federated learning, electronic health records, distributed optimization, sparse support vector machines, primal-dual splitting, binary classification, data privacy, multi-institutional collaboration, feature extraction, heart disease prevention
- MeSH terms
- Machine Learning, Algorithms, Hospitalization, Humans, ROC Curve, Databases, Factual, Electronic Health Records, Support Vector Machine
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