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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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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · cited 2×
“We consider SVMs because they are effective classifiers [14] and perform well in predicting hospitalizations.”
MethodSmith, D. K., & Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 12,385
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · cited 1×
“In particular, even though maintaining all data in a central location enables the implementation of anonymization measures (e.g., k-anonymity [13]), it introduces a single point of attack or failure and makes it possible for a data breach to expose identifiable data for many individuals.”
BackgroundGeneral Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557 · cited 1×
“[ 9–11] Two well-known examples of privacy breaches are the Netflix Prize and the Massachusetts Group Insurance Commission (GIC) medical records database.”
BackgroundA New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Weik, M. H. (2000) · Springer eBooks · doi:10.1007/1-4020-0613-6_12436 · Cited by 2,700
Statistical Learning with Sparsity
Hastie, T. J., Tibshirani, R. J., & Wainwright, M. J. (2015) · Chapman & Hall/CRC eBooks · doi:10.1201/b18401 · Cited by 2,529
Research works printed but not linked: 1
- R. Chen, I.C. Paschalidis, Outlier Detection Using Distributionally Robust Optimization under the Wasserstein Metric, ArXiv Prepr. ArXiv170602412 (2017).
Cited by
Sorted by citations
Items 701–800 of 994
QP-LDP for better global model performance in federated learning
Chen, Q., Chai, Z., Wang, Z., Chen, J., Yan, H., & Lin, X. (2022) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn57253.2022.00074 · Cited by 2
“T HE surge of massive data has led to significant interest in federated learning (FL) [1] in many applications, such as the keyboard application [2] and Healthcare [3], [4].”
BackgroundMTFL: Multi-task Federated Learning for Classification of Healthcare X-Ray Images
Kumar, P., Sinha, I. K., & Singh, K. P. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31417-9_43 · Cited by 2
An unsupervised embedding harmonization system for privacy-preserving data mining in healthcare
Li, M., Lin, Y., Chen, H., & Aparasu, R. R. (2023) · IISE Transactions on Healthcare Systems Engineering · doi:10.1080/24725579.2023.2231011 · Cited by 4
“On the other hand, distributed learning or federated learning provides another privacy-preserving way to jointly learn a data mining model from local databases without the need for data centralization (Brisimi et al., 2018; J. Lee et al., 2018; Li et al., 2019).”
BackgroundMLChain: a privacy-preserving model learning framework using blockchain
Bansal, V., Baliyan, N., & Ghosh, M. (2023) · International Journal of Information Security · doi:10.1007/s10207-023-00754-3 · Cited by 2
“Brisimi et al. [27] proposed a decentralized cluster primal-dual splitting (cPDS) algorithm which solves the sparse support vector machine problem and uses it to predict future hospitalizations.”
A Comparative Analysis of Federated Learning Towards Big data IoT with Future Perspectives
Govindaram, A., Jegatheesan, A., & Baburaj, E. (2023) · International Conference on Computing and Information Technology (ICCIT) · doi:10.1109/iccit58132.2023.10273901 · Cited by 2
“Electronic commerce, investment, and healthcare are three major areas of application [14].”
BackgroundConsensus modeling: Safer transfer learning for small health systems
Tourani, R., Murphree, D. H., Sheka, A. C., Melton, G. B., Kor, D. J., & Simon, G. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102899 · Cited by 2
Saputri, D. G., & Sriwana, I. K. (2024) · Journal of Technology and System Information · doi:10.47134/jtsi.v1i3.2578 · Cited by 2
“Perusahaan dapat membuat keputusan berbasis data yang tepat, mengoptimalkan rantai pasokan, dan menjaga keseimbangan stok yang sesuai (Blasco, 2017; Brisimi, 2018; He, 2017; Johnstone, 2018; Yan, 2019).”
BackgroundADLG: An Adaptive Deep Leakage from Gradients in Federated Learning
Zhang, Z., & Mo, L. (2024) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac63405.2024.10598543 · Cited by 2
“Nevertheless, within pertinent domains, such as finance [4] and medical imaging [5], the predominance of individual or corporate privacy considerations frequently takes precedence [6].”
BackgroundAnalysis of Techniques and Methods for Health Informatics: A Quantum Leap in Health Care
Balasubramaniam, A., & Surendiran, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5412-0_19 · Cited by 2
DAFL: Device-to-Device Transmissions for Delay-Efficient Federated Learning Over Mobile Devices
Su, H.-A., Prakash, P., Chen, R., Gong, Y., Yu, R., Fu, X., & Pan, M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3486545 · Cited by 2
“FL over mobile devices has prompted numerous applications, such as keyboard predictions [2], physical hazard detection in smart homes [3], health event detection [4], etc.”
Self-citationA Comprehensive Survey on Federated Learning and its Applications in Health Care
Pacheco, S. A. B. (2024) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet62352.2024.10730687 · Cited by 2
Yang, R., Wang, K., & Li, X. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ada6f2 · Cited by 2
“Currently, FL has been successfully applied in various fields, including smart healthcare [34], smartphones [35], and smart cities [36].”
FedDeepInsight—A privacy-first federated learning architecture for medical data
Duah, A. G., Bumbuc, R. V., Korkmaz, H. I., Wilding, R., & Sheraton, V. M. (2025) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2025.101691 · Cited by 2
Enabling Decentralized Clinical Insights
Gupta, S., Gupta, P., Goel, L., & Jain, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3306-9.ch006 · Cited by 2
Federated Learning at Mobile Edge Networks: A Tutorial
Lim, W. Y. B., Ng, J. S., Xiong, Z., Niyato, D. T., & Miao, C. (2022) · Wireless networks · doi:10.1007/978-3-031-07838-5_1 · Cited by 4
Federated Deep Learning Algorithm For Target Recognition Based On Trusted Network
Yang, J., Zheng, Y., Li, J., & Shen, F. (2021) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia53151.2021.9619633 · Cited by 2
Exploring investment strategies for federated learning infrastructure in medical care
Xing, J., Zhangy, X., Jiangz, Z., Zhangz, R., Zhaz, C., & Yinz, H. (2021) · IEEE International Conference on Computational Science and Engineering (CSE) · doi:10.1109/cse53436.2021.00034 · Cited by 2
Privacy Shield: A System for Edge Computing using Asynchronous Federated Learning
Khalid, A., Aziz, Z., & Fathi, M. S. (2022) · Scientific Programming · doi:10.1155/2022/7465640 · Cited by 2
“Implementation of federated learning in vehicle-tovehicle communication [9] and in medical applications [10] also falls under the category of synchronous learning.”
BackgroundIslam, H., Alaboud, K., Paul, T., Rana, M. K. Z., & Mosa, A. S. M. (2022) · PubMed Central · Cited by 2
Studies on Human Recognition Activities Based on Federated Learning
Xu, S., Liu, Y., & He, X. (2022) · International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) · doi:10.1109/icceai55464.2022.00084 · Cited by 2
Federated Ensemble Algorithm Based on Deep Neural Network
Wang, D., & Wang, T. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-0405-1_6 · Cited by 2
Addressing Class Variable Imbalance in Federated Semi-Supervised Learning
Dong, Z., Liu, W., Liu, S., & Chen, X. (2023) · International Conference on Computer Science, Engineering and Applications (CSEA) · doi:10.5121/csit.2023.130522 · Cited by 2
“However, at the data level, the data collected by different devices are scattered and variable due to the different geographical locations of the devices [5], and the data used for FL model training is class unbalanced.”
BackgroundBroad Federated Meta-Learning of Damaged Objects in Aerial Videos
Li, Z., & Wang, W. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.028670 · Cited by 2
Federated Learning with ResNet-18 for Medical Image Diagnosis
Wang, C. (2023) · International Conference on Multimedia Systems and Signal Processing (ICMSSP) · doi:10.1145/3613917.3613922 · Cited by 3
“AI training without sharing raw data [17] [15], it recently gained traction for healthcareapplications [13] [7] [20].”
BackgroundExpanding impact of mobile health programs: SAHELI for maternal and child care
Verma, S., Singh, G., Mate, A., Verma, P., Gorantla, S., Madhiwalla, N., Hegde, A., Thakkar, D., Jain, M., Tambe, M., et al. (2023) · AI Magazine · doi:10.1002/aaai.12126 · Cited by 2
Enhancing Privacy Preservation in Federated Learning via Learning Rate Perturbation
Wan, G., Du, H., Yuan, X., Yang, J., Chen, M., & Xu, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00440 · Cited by 2
Task Merging and Scheduling for Parallel Deep Learning Applications in Mobile Edge Computing
Long, X., Wu, J., Wu, Y., & Chen, L. (2019) · International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) · doi:10.1109/pdcat46702.2019.00022 · Cited by 2
“For example, federated learning [7] runs a small model at local to processor real-time data while the aggregation and update of model parameters are performed on the edge server.”
BackgroundDiddee, H., & Kansra, B. (2020) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3324884.3418911 · Cited by 2
“Specifically in the healthcare sector, the lack of covariate data can directly result in limited and biased diagnosis [5] [17] [12] [14] [1].”
BackgroundTowards Federated Learning-based Collaborative Adaptive Cybersecurity for Multi-microgrids
Boudko, S., Abie, H., Nigussie, E., & Savola, R. M. (2021) · International Conference on Signal Processing and Multimedia Applications · doi:10.5220/0010580700830090 · Cited by 2
“In (Brisimi et al., 2018), a decentralized optimization framework was developed that enables multiple data holders to collaborate and converge to a common predictive model in a decentralized manner.”
Bakhtiari, M., Nasirigerdeh, R., Torkzadehmahani, R., Bayat, A., Blumenthal, D. B., List, M., & Baumbach, J. (2020) · arXiv (Cornell University) · Cited by 2
Comfetch: Federated Learning of Large Networks on Memory-Constrained Clients via Sketching.Preprint
Rabbani, T., Feng, B. Y., Yang, Y., Rajkumar, A., Varshney, A., & Huang, F. (2021) · arXiv (Cornell University) · Cited by 2
Federated Synthetic Learning from Multi-institutional and Heterogeneous Medical DataPreprint
Chang, Q., Yan, Z., Qu, H. Y., Zhang, H., Baskaran, L., Zhang, S., & Metaxas, D. (2021) · Research Square · doi:10.21203/rs.3.rs-965641/v1 · Cited by 2
Contents IMIA Yearbook of Medical Informatics 2019
(2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1694828 · Cited by 2
Cohort-based Federated Learning Services for Industrial Collaboration on the EdgePreprint
Hießl, T. (2021) · doi:10.36227/techrxiv.14852361 · Cited by 2
Babu, K. M., Bhavitha, E., Mythri, M., Anusha, A., Chandana, B. S., & Akhtar, C. G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5088943 · Cited by 2
Gouthamchand, V., Gottardelli, B., Kulkarni, G., Sherkhane, U. B., Hogenboom, J., Subramanian, R., Jha, A. K., Mithun, S., Purandare, N. C., Agarwal, J. P., et al. (2025) · medRxiv · doi:10.1101/2025.01.29.25321214 · Cited by 2
Lai, W., & Zheng, Y. (2023) · Electronic Research Archive · doi:10.3934/era.2023255 · Cited by 1
“In the literature [25], the acoustic model was improved and combined with CNN to build an acoustic model and optimize the model as a whole; in the literature [26], optimization methods and transfer learning were used to improve the recognition rate of South Chinese dialects; however, since Tsinghua University did not…”
MethodNdacayisaba, L. J., Mason, J. M., & Kühn, P. (2023) · Current cancer research · doi:10.1007/978-3-031-22903-9_7 · Cited by 1
Integration of Federated Learning to Smart Grid for Efficient and Secure Energy Distribution
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2023) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-34821-1_52 · Cited by 1
Diabetes Types Classification Through Federated Learning and Adversarial Robustness
Charu, K. H., Nafis, K. F. Z., & Reza, M. T. (2024) · Parul International Conference on Engineering and Technology (PICET) · doi:10.1109/picet60765.2024.10716058 · Cited by 1
A systematic review of the foundations, applications, and challenges of federated learning
Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1
TTFL: Towards Trustworthy Federated Learning with Arm Confidential Computing
Sun, L., Zhu, J., Chang, B., Xu, Y., Yang, B., Wu, H., Xu, F., & Zhong, S. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00132 · Cited by 1
“FL has been used in commerce [2], healthcare [3], and industry [4].”
Unwinding the Significance of Federated Learning for Smart Agriculture
Bhat, A. N., Sahil, & Kumar, G. (2024) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/globalaisummit62156.2024.10947983 · Cited by 1
“The authors demonstrate the effectiveness of this approach by showing that predictive models trained using federated learning can achieve comparable accuracy to those trained on centralized datasets [23].”
Butt, H. A., Rashid, Z., Ahad, A., Yousaf, A., & Imran, I. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0593-6.ch013 · Cited by 2
Future Trends in Artificial Intelligence for Medical Applications
Chaudhuri, T., & Ghosh, J. (2025) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-9816-6.ch020 · Cited by 1
Chen, X., Talisa, V. B., Tan, X., Qi, Z., Kennedy, J. N., Chang, C.-C. H., Seymour, C. W., & Tang, L. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2017 · Cited by 1
Benhammou, Y., Kalyan, S., & Kumar, S. (2026) · International Conference on Federated Learning and Intelligent Computing Systems (FLICS) · doi:10.1109/flics70075.2026.11621962 · Cited by 1
“FL has been applied to mobile computing [6], finance [7], and healthcare [8].”
Integrating IoMT and Federated Learning for Advanced Healthcare Monitoring in Healthcare 5.0
Almogadwy, B. S., & Alqarafi, A. (2025) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010380715250728143022 · Cited by 1
Mali, S., Mali, N., Zeng, F., & Zhang, L. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03130-4 · Cited by 1
Cacciatore, A., Cosmo, M. D., Frontoni, E., & Bernardini, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06118-8_20 · Cited by 2
Federated Machine Learning for Cardiovascular Risk Assessment: A Decentralized XGBoost Approach
Alom, M. S., Akhi, S. S., Borsha, S. N., Mia, N., Tamim, F. S., & Nabin, J. A. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11171956 · Cited by 1
“Brisimi et al. [19] applied FL to Electronic Health Records (EHR) for detecting comorbidities and chronic conditions, demonstrating real-world viability in hospital networks.”
A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models
Ye, W., Wang, W., Liu, Y., Song, Y., Ren, B., Bi, W., Cucchiara, R., & Sebe, N. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3621246 · Cited by 2
Framework for co-distillation driven federated learning to address class imbalance in healthcare
Racha, S., Gupta, S., Firdowse, H., Solanki, A., Ramakrishnan, G., & Jadhav, K. S. (2025) · Discover Data · doi:10.1007/s44248-025-00079-7 · Cited by 3
“Recent works also developed FL architectures using SVM and distributed learning for medical domain[7, 8].”
Architectural patterns for blockchain-enabled federated learning: A systematic literature review
Wang, J., Shi, D., Li, S., Yan, J., Wang, Y., Mao, Q., Liu, Y., Zhu, L., & Zhang, H. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112695 · Cited by 1
Vanishing Privacy: Fast Gradient Leakage Threat to Federated Learning
Jiang, Y., Liu, X., & Yan, Q. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228471 · Cited by 1
Prototypical clustered federated learning for heart rate prediction
Yin, Y., Ruan, H., Chen, Y., Chen, J., Li, Z., Su, X. J., Zhou, Y., & Gong, Q. (2025) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2500062 · Cited by 1
Liu, L., Chen, J., Fan, H., Xu, K., Zhang, P., Li, X., & Xiang, T. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3645450 · Cited by 1
“Third, privacy demands in fields such as healthcare [22], [23] emphasize the critical necessity of secure and legally compliant data usage.”
A collaborated federated learning for healthcare informatics: solution and challenges
Joshi, R., Kumari, S., Pandey, K., & Badola, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33789-5.00024-8 · Cited by 1
Accelerating Federated Edge Learning via Wireless and Heterogeneity Aware Subnetwork Scheduling
Li, L., Geng, J., Su, H.-A., Qin, X., Hou, Y., Wang, H., Fu, X., & Pan, M. (2025) · IEEE Transactions on Networking · doi:10.1109/ton.2025.3643831 · Cited by 1
“…of ending edge devices and FL’s fundamental principle of preserving data privacy, FL over mobile devices has paved the way for a diverse spectrum of innovative mobile applications, including keyboard predictions [5], smart home hazard detection [6], health event detection [7], and so on.”
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Németh, G. D., Fanì, E., Ng, Y. J., Barbara Caputo, B., Lozano, M. Á., Oliver, N., & Quadrianto, N. (2025) · International Conference on Federated Learning Technologies and Applications (FLTA) · doi:10.1109/flta67013.2025.11336421 · Cited by 1
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Zhang, Y., Lin, D., Zhao, Y., & Guan, Y. (2025) · International Symposium on AI and Cybersecurity (ISAICS) · doi:10.1109/isaics66888.2025.11350255 · Cited by 1
“It has been successfully applied to speech recognition [3], medical diagnosis [4], [5], and cooperative IoT applications [6], [7], [8], [9], [10].”
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Baghel, R. S., & Mamodiya, U. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_40 · Cited by 1
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Rajput, K. G., Sharma, A., Jacob, B. S., & Devendrabhai, P. A. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_7 · Cited by 1
Model centric collaboration reduces data sharing barriers in medical artificial intelligence
Dai, Y., Cai, Y., Xu, Y., Cai, Q., Zhang, S., Wang, Y., Xie, Y., Liu, Y., & Cheng, L. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-00914-z · Cited by 2
“By treating models themselves as the carriers of data-derived knowledge, healthcare institutions can participate in global collaborative learning while preserving data sovereignty and regulatory compliance [34, 35].”
sali, R., & Harikrishna, T. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11383196 · Cited by 1
“To address this situation, Brisimi, T.S., et al [26] present a decentralized optimization framework that allows many data holders to collectively train models without sharing raw data.”
Kundu, R., Shi, X., Kleinsasser, M. J., Fritsche, L. G., Salvatore, M., & Mukherjee, B. (2026) · Biostatistics · doi:10.1093/biostatistics/kxag001 · Cited by 1
Yan, Z., Yao, Y., Wen, X., Zhang, S., Zhang, J., & Fan, K. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-21171-2_11 · Cited by 2
Federated Learning in Privacy Preservation and Security Enhancement for e-Healthcare Systems
Baidya, A., Jha, R. K., Modak, S., & Ghosh, S. (2026) · Springer optimization and its applications · doi:10.1007/978-3-032-15092-9_13 · Cited by 1
Federated Learning Applications in Healthcare Informatics: A Comprehensive Review
Howlader, S. M. R. K., Liu, L., Chen, X., Wu, H., Yuan, B., & Bhattacharjee, A. (2026) · ACM Computing Surveys · doi:10.1145/3809485 · Cited by 2
“Brisimi et al. introduced a decentralised FL model, the Cluster Primal Dual Splitting (cPDS) method, to predict hospitalisation in heart disease patients using EHRs [22].”
Artificial intelligence and radiomics in precision oncology
Shafeeque, M. A., Fatima, N., Khan, M. A., Rashid, G., Singh, T., Zaidi, N., & Siddiqui, Z. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00015-3 · Cited by 1
Aarthi, S., & Gohil, J. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8889-2.ch013 · Cited by 1
Sirapangi, M., Gopikrishnan, S., Herencsár, N., Li, M., & Srivastava, G. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3693339 · Cited by 1
“In healthcare, applications of FL have focused on cross-institutional collaborations [19], [20], enabling models to learn from distributed clinical records while adhering to regulatory requirements such as HIPAA and GDPR.”
IoT-Enabled Federated TabNet Framework for Privacy-Preserving CVD Risk Prediction
Zahedi, M., Habiba, Z., Sarkar, B., & Kandar, S. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.335 · Cited by 1
Lee, K., Kumagai, C., Komuro, M., Miyo, K., & Hoshimoto, H. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02447-5 · Cited by 1
LDA-Enhanced Federated Learning for Image Classification with Missing Modality
Sun, X., & Wang, X. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-5188-5_17 · Cited by 1
Decentralised and Privacy Preserving Machine Learning for Multiple Distributed Data Resources
Alkhozae, M., & Zeng, X. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-87094-2_21 · Cited by 1
Privacy-Preserving Distributed Support Vector Machines
Bottoni, S., Braghin, S., Brisimi, T. S., & Trombetta, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-93663-1_8 · Cited by 1
Self-citationPezoulas, V. C., Exarchos, T. P., & Fotiadis, D. I. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816507-2.00001-3 · Cited by 1
Study of Contribution Verifiability for Privacy-preserving Federated Learning
Hsu, R., Kao, S.-W., & Huang, T.-Y. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-3-031-05491-4_26 · Cited by 1
A Privacy-Preserving Federated Learning with Mutual Verification on Vector Spaces
Zhang, M., Cui, C., Shen, G., & Zhang, Y. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7242-3_14 · Cited by 1
Adaptive quantized online distributed stochastic mirror descent algorithm
Xie, J., Gao, W., & Xie, Y. (2022) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac57282.2022.10023617 · Cited by 1
Zhang, Z., Zhang, C., Chen, G., Xiao, S., & Huang, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36049-7_19 · Cited by 1
Haritas, H. K., Haritas, C. K., & Kallimani, J. S. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3982-4_22 · Cited by 1
Non-Cryptographic Privacy Preserving Machine Learning Methods: A Review
Şahinbaş, K., Çatak, F. Ö., Kuzlu, M., Tabassum, M., & Sarp, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-50920-9_32 · Cited by 1
Personalized Multi-task Federated Learning on Non-IID Medical Health Care Data
Feng, Y., Chang, X., & Zhao, J. (2023) · International Conference on Big Data Computing and Communications (BigCom) · doi:10.1109/bigcom61073.2023.00025 · Cited by 1
“…medical big data can not only train and generate disease prediction models, but also derive potential application scenarios in more dimensions, including triage and diagnosis, chronic disease prevention and control, disease early screening, medical insurance cost control and other directions [6].”
BackgroundDAFL: Delay Efficient Federated Learning over Mobile Devices via Device-to-Device Transmissions
Su, H.-A., Prakash, P., Chen, R., Gong, Y., Yu, R., Fu, X., & Pan, M. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437066 · Cited by 1
“FL over mobile devices has prompted numerous applications, such as keyboard predictions [2], physical hazard detection in smart homes [3], health event detection [4], etc.”
BackgroundSelf-citationFSVNet: Federated Self-driving Vehicle Network based on decomposition global convergence
Bonyani, M., & Rad, A. B. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.059 · Cited by 1
Jin, Z., Xu, X., Bilal, M., Wu, S., & Lin, H. (2024) · Computational Intelligence · doi:10.1111/coin.12684 · Cited by 1
Edge Implementation of Unsupervised Self-evolving Vision Classifier
Aghasanli, A., & Angelov, P. P. (2024) · IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) · doi:10.1109/eais58494.2024.10570024 · Cited by 1
“This strategy is especially applicable in the healthcare sector [18], where the protection of patient data is of utmost importance, and in mobile computing, where limitations on bandwidth and expenses related to data transmission are crucial factors.”
BackgroundCybersecurity Challenges, Best Practices, and Future Work in Healthcare
Sharma, D. P., Lashkari, A. H., & Parizadeh, M. (2024) · Progress in IS · doi:10.1007/978-3-031-68034-2_10 · Cited by 1
Federated Learning Methodology for Indoor Location Systems in Multi-Layer Architecture
Ruiz, J. L. L., Espinosa, Á. V., & Espinilla, M. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651450 · Cited by 1
“In hospital resource management [19], it is intended to predict future hospitalizations of patients with heart disease or their mortality.”
BackgroundFederated learning in healthcare: A systematic review of evolution from traditional paper records
Garg, R., Varia, M., Gorimar, U., Siganporia, A., & Gupta, N. (2024) · AIP conference proceedings · doi:10.1063/5.0228108 · Cited by 1
Mohamed, A. A., Saleh, Y. N. M., & Abdel-Hamid, A. (2024) · IEEE Access · doi:10.1109/access.2024.3502909 · Cited by 1
Big data on Occupational Health: how far are we?
Fagundes, T. P., Wichmann, R. M., Silva, M. E. D. L. E., & de Oliveira, T. A. (2024) · Revista Brasileira de Saúde Ocupacional · doi:10.1590/2317-6369/40522en2024v49edcinq11 · Cited by 2
A Federated Learning Framework for Intermittent Clients in Collaborative Smart Healthcare Systems
Turab, A., Ullah, F., & Zaidi, S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-0593-6.ch001 · Cited by 1
Veerapaneni, P. K. (2023) · INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY · doi:10.34218/ijrcait_06_01_009 · Cited by 1
Rethinking Blockchain and Decentralized Learning: Position Paper
Rahmadika, S., & Rhee, K.-H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-9343-7_18 · Cited by 1
Federated Learning-Based IDS Against Poisoning Attacks
Xu, M., & Li, X. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-96791-8_25 · Cited by 1
Stock Predictor with Graph Laplacian-Based Multi-task Learning
He, J., Tran, N. H. K., & Khushi, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08751-6_39 · Cited by 1
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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