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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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“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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Distributed Learning in Healthcare
Tuladhar, A., Rajashekar, D., & Forkert, N. D. (2022) · Integrated science · doi:10.1007/978-3-031-11199-0_10 · Cited by 11
Liu, T., Hu, X., & Shu, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3237806 · Cited by 10
“2) Backdoor Attack Accuracy: To explore the effectiveness of a backdoor before the FL model converges, we inject centralized backdoors for the baseline and our method at the global FL epochs 11, 16, and 21, respectively.”
BackgroundMethodShen, A., Francisco, L., Sen, S., & Tewari, A. (2023) · Journal of Medical Internet Research · doi:10.2196/43664 · Cited by 10
“Using electronic health records distributed across hospital systems, researchers have successfully developed federated models to predict heart-related hospitalizations [14], electrocardiogram classification [15], and clinical outcomes in patients with COVID-19 [16].”
Vo, V. T.-T., Shin, T.-H., Yang, H.-J., Kang, S., & Kim, S.-H. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108104 · Cited by 11
How Valuable is Your Data? Optimizing Client Recruitment in Federated Learning
Ruan, Y., Zhang, X., & Joe‐Wong, C. (2024) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2024.3422264 · Cited by 10
“Recent papers also demonstrated the power of federated learning for different applications [5], [18], [31].”
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Wang, Z., Chang, Z., Hu, J., Pang, X., Du, J., Chen, Y., & Ren, K. (2024) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom52122.2024.10621090 · Cited by 17
“As a privacy-preserving ML technology [2]–[9], FL provides services for various privacy-sensitive applications, including healthcare [10], word prediction [11], and finance [12].”
BackgroundFederated Learning: Challenges, SoTA, Performance Improvements and Application Domains
Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10
Federated Learning: An Approach for Managing Data Privacy and Security in Collaborative Learning
Jaswal, R., Panda, S. N., & Khullar, V. (2025) · Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) · doi:10.2174/0123520965328724241218110637 · Cited by 13
Panda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11
Minango, J., Mora, H. C., Zambrano, M., V, Garzón, N. O., & Pérez, F. J. (2025) · Technologies · doi:10.3390/technologies13020058 · Cited by 10
A federated learning model with the whale optimization algorithm for renewable energy prediction
Chifu, V. R., Cioara, T., Anitei, C. D., Pop, C. B., Anghel, I. M., & Toderean, L. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110259 · Cited by 11
Pirmani, A., Brouwer, E. D., Arany, Á., Oldenhof, M., Passemiers, A., Faes, A., Kalinčík, T., Özakbaş, S., Gouider, R., Willekens, B. M. P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01788-8 · Cited by 16
Engineering Federated Learning Systems: A Literature Review
Zhang, H., Bosch, J., & Olsson, H. H. (2021) · Lecture notes in business information processing · doi:10.1007/978-3-030-67292-8_17 · Cited by 10
Developments in Artificial Intelligence for Ophthalmology: Federated Learning
Teo, Z. L., Lee, A., Campbell, P., Chan, R. V. P., & Ting, D. S. W. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000582 · Cited by 13
Federated generalized linear mixed models for collaborative genome-wide association studies
Li, W., Chen, H., Jiang, X., & Harmanci, A. O. (2023) · iScience · doi:10.1016/j.isci.2023.107227 · Cited by 10
“Federated association testing (rooted from Federated Learning approaches [33, 34]) among different sites present a viable solution for increasing sample sizes while underlying genotype and phenotype data is not explicitly shared.”
BackgroundLiu, Y., Lin, T., & Ye, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122440 · Cited by 10
Towards Trustworthy Collaborative Healthcare Data Sharing
Firdaus, M., & Rhee, K.-H. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385319 · Cited by 13
“Moreover, the efficacy of FL is evident in various domains of learning, including predictive models in health [4], low latency vehicle-to-vehicle communication [5], vocabulary estimation [6], and next word prediction [7].”
BackgroundSakhawat, A. R., Fatima, A., Abbas, S., Ahmad, M., & Khan, M. A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-0839-4.ch012 · Cited by 10
DNet: An Efficient Privacy-Preserving Distributed Learning Framework for Healthcare Systems
Kulkarni, P. P., Kasyap, H., & Tripathy, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65621-8_9 · Cited by 9
Differentially-Private Federated Learning with Long-Term Constraints Using Online Mirror Descent
Odeyomi, O. T., & Zaruba, G. V. (2021) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit45174.2021.9518177 · Cited by 10
Privacy-aware multi-institutional time-to-event studies
Späth, J., Matschinske, J., Kamanu, F., Murphy, S. A., Zolotareva, O. I., Bakhtiari, M., Antman, E. M., Loscalzo, J., Brauneck, A., Schmalhorst, L., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000101 · Cited by 10
“genome-wide association studies [17,18], differential gene expression analysis [19], the analysis of electronic health records [20], or the prediction of patient outcomes with COVID-19 [21].”
MethodLin, Z., Zeng, B., Hu, H., Huang, Y., Xu, L., & Yao, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110396 · Cited by 13
Federated Learning for Clinical Event Classification Using Vital Signs Data
Lee, K. Y., & Rakhmiddin, R. (2023) · Multimodal Technologies and Interaction · doi:10.3390/mti7070067 · Cited by 10
Badr, E. (2021) · ACM Computing Surveys · doi:10.1145/3453657 · Cited by 11
“[19] introduced a federated platform optimized to predict hospitalizations of patients with heart diseases based on their health records.”
BackgroundSkin Lesion Intelligent Diagnosis in Edge Computing Networks: An FCL Approach
Shi, Y., Li, X., & Chen, S. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3595186 · Cited by 9
“To overcome this obstacle, some studies [4, 37, 45] investigated federated learning based medical image diagnosis, which obtains a global deep learning model by performing local training and parameter aggregation instead of transferring raw data.”
BackgroundFrameworks for Privacy-Preserving Federated Learning
Phong, L. T., Phuong, T. T., Wang, L., & Ozawa, S. (2023) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2023mui0001 · Cited by 9
Chiariotti, F., Mamidanna, P., Suman, S., Stefanović, Č., Farina, D., Popovski, P., & Dosen, S. S. (2024) · IEEE Signal Processing Magazine · doi:10.1109/msp.2024.3401403 · Cited by 9
“FL might help mitigate this risk [38] since in FL, each user equipment (either the robotic limb itself or a node at the edge of the network) computes its own updates to the learning model, which are then propagated in a compressed form to the central server that performs the integration and creates…”
BackgroundFedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems
Gao, J., & Li, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821859 · Cited by 11
Joshi, H., & Joseph, S. (2025) · Advances in Health Information Science and Practice · doi:10.63116/ubym3803 · Cited by 10
FGS-FL: Enhancing federated learning with differential privacy via flat gradient stream
Hu, J., & Zhang, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128273 · Cited by 9
SemFedXAI: A Semantic Framework for Explainable Federated Learning in Healthcare
Amato, A., & Branco, D. (2025) · Information · doi:10.3390/info16060435 · Cited by 13
Zhang, Q., Chen, S., & Liu, W. (2025) · Symmetry · doi:10.3390/sym17060823 · Cited by 11
Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML
Li, M., Laiu, P. M., Nichols, J. A., Huettel, M., Sikkema, I., Mathur, M., Hollifield, S., & Hankins, M. (2025) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot65859.2025.11105231 · Cited by 9
“In fact, many recent works on FL have been focusing on the protection of data privacy [17]–[19], including healthcare data [20], [21], audio recording data [22], [23] and household smart meter data [24], [25].”
Wettinger, S. B., Karađuzović-Hadžiabdić, K., Attard, R., Farrugia, R. C., Wolford, B. N., Chierici, M., Jurman, G., Alexiou, P., Peñalvo, J. L., Costa, R. S., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf526 · Cited by 9
“…via federated learning infrastructure that shares only model parameters or summary statistics between centres, avoiding direct data sharing [165, 166]; and (4) governance ensured through data use agreements, institutional data protection officers, and compliance with GDPR ethical and legal…”
Dave, D., Vyas, K., Jayagopal, J. K., Garcia, A., Erraguntla, M., & Lawley, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-22316-4 · Cited by 16
“…healthcare applications, like medical image processing [54-56], IoT-based smart healthcare applications [57], managing electronic health records (EHR) [58], disease prediction [59-61], predict hospitalizations and mortality [62-64], natural language processing from clinical notes [65-67], etc.”
Jin, H., Yan, N., & Mortazavi, M. (2020) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1109/bdcat50828.2020.00021 · Cited by 10
Federated Learning - Methods, Applications and beyond
Heusinger, M., Raab, C., Fabrice Rossi, A., & Schleif, F.-M. (2021) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2021.es2021-4 · Cited by 12
“Applications of FL are more and more frequent [6, 7, 8, 9].”
BackgroundFedDNA: Federated learning using dynamic node alignment
Wang, S., & Xingquan Zhu, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288157 · Cited by 9
“More specifically, health data from an EHR system consisting of patient smartphones and distributed hospitals is trained locally on demographic information such as age, gender and physical characteristics [6].”
BackgroundContinual adaptation of federated reservoirs in pervasive environments
Caro, V. D., Gallicchio, C., & Bacciu, D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126638 · Cited by 9
Iterative and mixed-spaces image gradient inversion attack in federated learning
Fang, L., Wang, L., & Li, H. (2024) · Cybersecurity · doi:10.1186/s42400-024-00227-7 · Cited by 11
“…learning paradigm, which can collaboratively train a shared model in distributed system, has been widely studied and applied in privacy-sensitive learning tasks, such as next-word prediction (Hard et al. 2018), medical data analysis (Brisimi et al. 2018) or vision object (image) classification.”
BackgroundLuo, C., Chen, X., Xu, J., & Zhang, S. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101347 · Cited by 9
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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