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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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BackgroundPrivacy-Preserving Power Consumption Prediction Based on Federated Learning with Cross-Entity Data
Liu, H., Zhang, X., Shen, X., & Sun, H. (2022) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc55256.2022.10033866 · Cited by 3
“Federated learning was also applied in smart mobile devices [7], healthcare [8] and Internet of Things [9].”
MethodOn the Trade-off Between Benefit and Contribution for Clients in Federated Learning in Healthcare
Düsing, C., & Cimiano, P. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00257 · Cited by 3
“Federated learning (FL) is thus a promising paradigm that allows clients to profit from the benefits of a joint model while not having to share their data directly [6], [7].”
BackgroundTowards Building a Global Robust Model for Heart Disease Detection
Mali, B., Saha, S., Brahma, D., Pinninti, R., & Singh, P. K. (2023) · SN Computer Science · doi:10.1007/s42979-023-02083-7 · Cited by 5
“We have also seen some similar works where the models were [3, 17, 18] trained using federated learning concepts.”
BackgroundMethodResultInfluentialPPFL-IDS: Privacy-Preserving Federated Learning Based IDS Against Poisoning Attacks
Xu, M., & Li, X. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02231-6 · Cited by 3
“Brisimi et al. [26]. proposed a can use distribution between different data source/agent of EHR data to predict future hospitalization in patients with heart disease, a federated learning model, the article puts forward the original dual division (CPDS) clustering algorithm, and can solve the…”
Federated Similarity-Based Learning with Incomplete Data
Pȩkala, B., Szkoła, J., Dyczkowski, K., & Wilbik, A. (2023) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz52849.2023.10309757 · Cited by 3
“Such a process of model training has been successfully applied in various use cases, especially involving data collaboration in the medical sector [1], [2].”
BackgroundMethodValidation of Federated Unlearning on Collaborative Prostate Segmentation
Fraboni, Y., Innocenti, L., Antonelli, M., Vidal, R., Kameni, L., Sebastien Ourselin, A., & Lorenzi, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_31 · Cited by 3
Xu, B., Zhang, Y., Fan, Z., Han, L., & Shen, Z.-X. (2023) · Information & Management · doi:10.1016/j.im.2023.103908 · Cited by 3
Pezoulas, V. C., Kalatzis, F., Exarchos, T. P., Goules, A. V., Tzioufas, A. G., & Fotiadis, D. I. (2024) · Patterns · doi:10.1016/j.patter.2023.100893 · Cited by 3
“…algorithms have been proposed, including the family of the multiple additive regression trees (MART), 14–16 the support vector machines (SVM), 17,18 and the multinomial naive Bayes, 14 where the gradient boosting trees (GBT) algorithm is the most popular implementation of MART with favorable…”
MethodDetecting Lung Cancer with Federated and Transfer Learning
Mostafa, G., Hamidi, M. S., & Farid, D. M. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441256 · Cited by 4
“Brisimi et al. [15] propose an FL approach for predictive modeling from electronic health records (EHRs).”
BackgroundFedQL: Q-Learning Guided Aggregation for Federated Learning
Mei, C., Zhao, M., Zhang, T., Yu, N., & Lǚ, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0834-5_16 · Cited by 4
Mitigating Distributed Backdoor Attack in Federated Learning Through Mode Connectivity
Walter, K., Mohammady, M., Nepal, ., & Kanhere, S. S. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3637682 · Cited by 4
Jiang, C., Zhou, H., Zhang, X., & Chakraborty, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70352-2_11 · Cited by 4
Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning
Wang, H., Zhang, H., Wang, L., Xuan, S., & Zhang, Q. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3703631 · Cited by 3
“FL protects client privacy and is suitable for data-sensitive domains like health care [6, 7], and finance [8, 9].”
A Bibliometric Analysis on Federated Learning
Algorabi, Ö., Türkan, Y. S., Ulu, M., & Namlı, E. (2024) · Journal of Advanced Research in Natural and Applied Sciences · doi:10.28979/jarnas.1555351 · Cited by 4
Federated Mining of Interesting Association Rules Over EHRs
Molina, C., Prados-Suárez, B., & Sánchez, B. M. (2021) · Studies in health technology and informatics · doi:10.3233/shti210799 · Cited by 3
“neural networks weights by means of Federated Average [2], or Support Vector Machines planes [3]).”
MethodRobust Model Aggregation for Federated Learning with Heterogeneous Clients
Xu, R., Feng, X., & Zheng, H. (2021) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/iccc54389.2021.9674541 · Cited by 3
Generative Policies for Coalition Systems - A Symbolic Learning Framework
Bertino, E., White, G., Lobo, J., Ingham, J., Cirincione, G. H., Russo, A., Law, M., Calo, S. B., Manotas, I., Verma, D. C., et al. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00158 · Cited by 3
“sharing insights among different Government agencies [35], distributed electronic health records [36], machine translation [37], and intelligence in wireless networks [38].”
BackgroundTat, E., & Rabbat, M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00017-8 · Cited by 3
Blockchain Federated Learning Framework for Privacy-Preservation
Sameera, K. M., Vinod, P., Rehiman, K. A. R., Jifhna, P. N., & Sebastian, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23095-0_18 · Cited by 3
Federated Natural Language Generation for Personalized Dialogue SystemPreprint
Lu, Y., Huang, C., Zhan, H., & Zhuang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06419 · Cited by 3
“…the development of the these pragmatic fields, such as (Liu et al. 2020a) for vision-and-language grounding problems, (Kim et al. 2021) for face recognition, (Zhang et al. 2020a) for device failures detection in IIoT and (Brisimi et al. 2018; Kaissis et al. 2020) for sensitive records analysis.”
A Survey of Image Gradient Inversion Against Federated LearningPreprint
Li, Z., Wang, L., Chen, G., Shafq, M., & Gu, Z. (2022) · doi:10.36227/techrxiv.18254723.v1 · Cited by 3
A Novel Privacy-Preserved Recommender System Framework Based On Federated Learning
Qin, J., Liu, B., & Qian, J. (2021) · International Conference on Software Engineering and Information Management (ICSIM) · doi:10.1145/3451471.3451485 · Cited by 27
“Currently, the study of FL has been carried out in many application fields [9][10][11] related to privacy protection and data security.”
BackgroundRuntime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis
Zhang, X., Xue, X., Du, X., Xie, X., Liu, Y., & Sun, M. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3550330 · Cited by 5
“This design enhances data privacy and enables many privacy-sensitive applications, including the predictive model learning in healthcare where the confidentiality of patient records across decentralized hospitals must be enforced [2], [3], [4].”
Noise-Robust Federated Learning via Interclient Co-Distillation
Gao, L., Li, L., Chen, Y., Fu, S., Wang, D., Wang, S., Xu, C., & Xu, M. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3546903 · Cited by 3
“4) Rigorous experimental validation across multiple datasets with varied noise configurations, attesting to FedDQ ’s superiority over state-of-the-art benchmarks.”
Practical Federated Learning without a Server
Dhasade, A., Kermarrec, A.-M., Lavoie, E., Pouwelse, J. A., Sharma, R. K., & de Vos, M. (2025) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3721146.3721938 · Cited by 2
Enhancing Federated Feature Selection Through Synthetic Data and Zero Trust Integration
Madathil, N. T., Alrabaee, S., & Belkacem, A. N. (2025) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2025.3560037 · Cited by 2
“In [30], the authors developed an FL-based model for predicting hospitalization-prone individuals using a clustering-based dual splitting approach, achieving comparable accuracy in classifier prediction.”
A Comprehensive Review of Federated Learning in Cancer Diagnosis and Prognosis Prediction
Reddy, K. D., Anusha, S., & Ashalatha, N. (2025) · IgMin Research · doi:10.61927/igmin294 · Cited by 2
Tran, H. H.-V., Thu, A., Fuertes, A., Twayana, A. R., Mahadevaiah, A., Mehta, K. A., James, M., Basta, M., Weissman, S. I., Frishman, W. H., et al. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000000975 · Cited by 4
Efficient verifiable secure aggregation protocols for federated learning
Xu, B., Wang, S., & Tian, Y. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104161 · Cited by 2
Mensah, S. O., Djoumessi, K., & Berens, P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04981-0_44 · Cited by 3
Yang, A. C., Pan, M.-L., Lu, H. H., Lien, C.-Y., Wang, D.-W., Chen, C., Tarng, D., Niu, D., Chiou, S.-H., Wu, C.-Y., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/58954 · Cited by 4
“This approach has been used in various medical domains, including oncology and radiology, for tasks such as image analysis and disease prediction [81,82].”
Federated Learning: A Paradigm Shift in Healthcare Data Privacy
Goel, A., & Prabha, C. (2026) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-96265-3_12 · Cited by 2
Safeguarding Federated Learning From Data Reconstruction Attacks via Gradient Dropout
Sotthiwat, E., Zhang, C., Xiao, X., & Zhen, L. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3659401 · Cited by 3
“This framework is particularly important in privacy-sensitive domains, such as healthcare [2], [3], [4] and banking [5], [6], where regulations strictly prohibit the sharing of personal medical records or financial transactions.”
A secure cross-domain federated learning scheme based on blockchain fair payment
Li, Q., Xia, D., Tian, Y., & Quanxing, Z. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3589 · Cited by 2
Verma, R. K. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_28 · Cited by 2
A privacy-preserving multi-user retrieval system for multimodal artificial intelligence
Gao, Y., Luo, W., Wang, C., Ahmad, N. S., Wang, X., & Goh, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-40734-w · Cited by 4
Zhao, Z., Alzubaidi, L., Alwzwazy, H. A., Zhang, J., Alammar, Z., & Gu, Y. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11540-7 · Cited by 2
Abady, E. M. A., Elewa, M., Elrefaey, H. A., Mathew, K. T., Tamvakologos, P. I., Kuhn, K., & Alsabri, M. (2026) · Sage Open Pediatrics · doi:10.1177/30502225261445743 · Cited by 2
“Acosta et al 92 95 and Dayan et al 96 demonstrated feasibility for clinical applications, with federated models achieving comparable performance to centrally trained models while maintaining data privacy.”
Li, Q., Gao, M., Zhai, W., Wu, W., Wang, C., & Abdelmoniem, A. M. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116300 · Cited by 2
Vehicular Networks and Autonomous Driving Cars
Hong, C. S., Khan, L. U., Chen, M., Chen, D., Saad, W., & Han, Z. (2021) · Wireless networks · doi:10.1007/978-981-16-4963-9_8 · Cited by 2
Jin, Y., Zhu, H., Xu, J., & Chen, Y. (2022) · Machine learning · doi:10.1007/978-981-19-7083-2_4 · Cited by 2
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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