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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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BackgroundPersonalized Federated Learning for In-Hospital Mortality Prediction of Multi-Center ICU
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“With such desirable properties, FL over mobile devices has inspired a wide utilization in a large variety of intelligent services, such as the keyword prediction [1], voice classifier [2], and e-health [3], etc.”
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Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms.
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The value of federated learning during and post-COVID-19
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“Such decentralized studies will benefit greatly if FL is incorporated in the study design and data analysis to evaluate quality of care and outcomes like predicting mortality, complications, hospitalizations, and adverse drug events.(7-9) Another promising area for FL is digital health.”
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Federated Learning for Privacy-Aware Human Mobility Modeling
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“Brisimi et al. (2018) applied FL on electronic health records to develop predictive models for heart-related hospitalizations.”
MethodDecentralised Federated Learning for Hospital Networks With Application to COVID-19 Detection
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“Reference [10] uses a FL setting based on the soft-margin l1-regularized sparse Support Vector Machine (sSVM) classifier and on an iterative cluster”
MethodHeterogeneous Training Intensity for Federated Learning: A Deep Reinforcement Learning Approach
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Federation Opacity and the Promise of Federated Learning in Healthcare
Hatherley, J. J., Søgaard, A., Ballantyne, A., & Pauwels, R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2637093 · Cited by 27
“…et al. 2024; Danek et al. 2024; Haggenmüller et al. 2024; Rehman et al. 2024; Soltan et al. 2024; Stripelis et al. 2024) • risk and outcome prediction (Brisimi et al. 2018; Huang et al. 2019; Dayan et al. 2021; Ogier Du Terrail et al. 2023; Lin et al (Kim et al. 2017) • population health…”
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“Some examples of successful federated learning in medical literature include its use in predicting future hospitalizations of patients with heart diseases using EHR and in COVID-19 diagnosis using X-Ray and ultrasound images [69,70] .”
MethodIHVFL: a privacy-enhanced intention-hiding vertical federated learning framework for medical data
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“Brisimi et al (2018) used the cluster Primal Dual Splitting (cPDS) algorithm to predict whether a patient with heart disease will be hospitalized.”
BackgroundMethodNanavaty, A., Sharma, R., Pandita, B., Goyal, O., Rallapalli, S., Mandal, M., Singh, V. K., Narang, P., & Chamola, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-79793-2 · Cited by 29
“For instance, a federated learning approach was used to predict hospitalizations for cardiac events using a binary classification supervised model 33 .”
RoFL: Attestable Robustness for Secure Federated Learning.Preprint
Burkhalter, L., Lycklama, H., Viand, A., Küchler, N., & Hithnawi, A. (2021) · arXiv (Cornell University) · Cited by 20
FedJAX: Federated learning simulation with JAXPreprint
Ro, J. H., Suresh, A. T., & Wu, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02117 · Cited by 54
“Federated learning has demonstrated usefulness in a variety of contexts, including next word prediction [Hard et al., 2018, Yang et al., 2018] and healthcare applications [Brisimi et al., 2018].”
Sana, T. Z., Abdulla, S., Nag, A., Das, A., Hassan, M. M., Fiza, Z. Z., Karim, A., & Kabir, S. R. R. (2025) · IEEE Access · doi:10.1109/access.2025.3605165 · Cited by 25
“Brisimi et al., [64] developed a decentralized optimization framework for cardiac hospitalization prediction that preserves data privacy while achieving comparable accuracy to centralized approaches.”
van Bragt, J. J. M. H., Hansen, S., Djukanović, R., Bel, E. H., Brinke, A. T., Wagers, S. S., der Zee, A. H. M., & Porsbjerg, C. M. (2021) · ERJ Open Research · doi:10.1183/23120541.00064-2021 · Cited by 18
A Federated Learning Paradigm for Heart Sound Classification
Qiu, W., Qian, K., Wang, Z., Chang, Y., Bao, Z., Hu, B., Schuller, B. W., & Yamamoto, Y. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871319 · Cited by 19
“The advantages of FL have recently been demonstrated in several practical applications, including medical image diagnosis [7], [9], prediction of disease risk factors [1], [10], and personalised medicine [8], which are particularly important in medical data applications.”
BackgroundOn a Framework for Federated Cluster Analysis
Stallmann, M., & Wilbik, A. (2022) · Applied Sciences · doi:10.3390/app122010455 · Cited by 20
“, for next-word predictions on smartphones [3], vehicle image classification [4], data collaboration in the healthcare industry [5,6], on IoT data [7–9], and many more.”
BackgroundCan we share models if sharing data is not an option?
Li, Z., Mao, F., & Wu, C. (2022) · Patterns · doi:10.1016/j.patter.2022.100603 · Cited by 20
“23–26 In the first round of FL, the central server sends the initial model to data owners, and the data owners train the model using local data for some epochs.”
MethodResultFedClamp: An Algorithm for Identification of Anomalous Client in Federated Learning
Manzoor, H. U., Khan, M. S., Khan, A. R., Ayaz, F., Flynn, D., Imran, M. A., & Zoha, A. (2022) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs202256217.2022.9970909 · Cited by 17
“FL is currently being used for emoji perdition in mobile phones [8], in smart devices to predict human trajectory [9]; in healthcare applications to proactively identify health anomalies [10] and predict hospital stay time [11].”
MethodVariance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning
Zhang, J., Liu, H., So, A. M., & Ling, Q. H. (2023) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2023.3240652 · Cited by 21
“3240652 learning [1], [2], privacy-preserving learning [3], [4], decentralized system control [5], [6], etc.”
Malik, H., & Anees, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-18065-z · Cited by 20
Visual–language foundation models in medicine
Liu, C., Jin, Y., Guan, Z., Li, T., Qin, Y., Qian, B., Jiang, Z., Wu, Y.-L., Wang, X., Zheng, Y. F., et al. (2024) · The Visual Computer · doi:10.1007/s00371-024-03579-w · Cited by 25
“By training models across decentralized devices or institutions, FL aggregates local updates to build a collective intelligence without cen-tralizing sensitive data [141, 142].”
Local Adaptivity in Federated Learning: Convergence and ConsistencyPreprint
Wang, J., Xu, Z., Garrett, Z., Charles, Z., Liu, L., & Joshi, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02305 · Cited by 19
AccDFL: Accelerated Decentralized Federated Learning for Healthcare IoT Networks
Wei, M., Yu, W., & Chen, D. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3486122 · Cited by 23
“…of consensus-based DFL frameworks enables Healthcare Internet of Things Networks (HIoTNs) to swiftly adapt to new electric healthcare records (EHRs), providing real-time clinical support and facilitating decision making, ultimately enhancing patient care and treatment outcomes [23], [24], [25].”
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling
Yue, X., & Kontar, R. A. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3355289 · Cited by 18
“To date, FedAvg for decentralized learning of deep neural networks (NN) was tailored to image classification, text prediction, wireless network analysis, and condition monitoring & failure detection [4], [5], [6], [7], [8], [9].”
MethodVREFL: Verifiable and Reconnection-Efficient Federated Learning in IoT scenarios
Ye, H., Liu, J., Zhen, H., Jiang, W., Wang, B., & Wang, W. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103486 · Cited by 18
Przybyła‐Kasperek, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109612 · Cited by 18
Raimondi, D., Chizari, H., Verplaetse, N., Löscher, B.-S., Franke, A., & Moreau, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-46887-2 · Cited by 19
Pan, W., Wang, X., Zhou, P., & Lin, W. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3345366 · Cited by 18
“For example, Google keyboard uses FL to enhance the next word predictions [9], while [10] utilizes FL to predict hospitalizations in the medical field.”
MethodFederated clustered multi-domain learning for health monitoring
Jiang, S., Li, Y., Firouzi, F., & Chakrabarty, K. (2024) · Scientific Reports · doi:10.1038/s41598-024-51344-9 · Cited by 20
Maliakel, P. J., Ilager, S., & Brandić, I. (2024) · International Workshop on Edge Systems, Analytics and Networking (EdgeSys) · doi:10.1145/3642968.3654813 · Cited by 18
“Such models can have serious consequences in application domains where fairness and unbiased predictions are crucial [9].”
BackgroundQiu, W., Feng, Y., Li, Y., Chang, Y., Qian, K., Hu, B., Yamamoto, Y., & Schuller, B. W. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3428512 · Cited by 18
“Examples include predicting hospitalisation rates of CVD patients [21], drug resistance [22], intensive care unit (ICU) stay times [23], ICU mortality, patient similarity, patient representation, and phenotyping [24].”
BackgroundEarly Detection of Type-2 Diabetes Using Federated Learning
Lincy, M., & Kowshalya, A. M. (2020) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset207644 · Cited by 18
Improving the Transient Times for Distributed Stochastic Gradient Methods
Huang, K., & Pu, S. (2022) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2022.3201141 · Cited by 20
“Distributed optimization problems in the form of (1) find applications in distributed machine learning [3], [4], [5], [6], [7], [8], [9], multiagent target seeking [10], and wireless networks [6], [11], [12], among many other scenarios.”
Pan, J., Fan, Z., Smith, G. E., Guo, Y., Bian, J., & Xu, J. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70128 · Cited by 19
FedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Fu, S., Jia, H., Vassilaki, M., Keloth, V. K., Dang, Y., Zhou, Y., Garg, M., Petersen, R. C., St Sauver, J., Moon, S., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104623 · Cited by 17
Securing Federated Sensitive Topic Classification against Poisoning Attacks
Chu, T., García-Recuero, Á., Iordanou, C., Smaragdakis, G., & Laoutaris, N. (2023) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2023.23112 · Cited by 19
“FL is also applied in healthcare to predict diseases [49], [50], detect patient similarity [51] while overcoming any privacy constrains.”
BackgroundFedHEONN: Federated and homomorphically encrypted learning method for one-layer neural networks
Fontenla-Romero, Ó., Guijarro‐Berdiñas, B., Hernández-Pereira, E., & Pérez‐Sánchez, B. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.018 · Cited by 17
Auction-based client selection for online Federated Learning
Guo, J., Li-na, S., Liu, J., Ding, J., Liu, X., Huang, B., & Li, L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102549 · Cited by 19
Decision Trees in Federated Learning: Current State and Future Opportunities
Heiyanthuduwage, S. R., Altas, I., Bewong, M., Islam, M. Z., & Deho, O. B. (2024) · IEEE Access · doi:10.1109/access.2024.3440998 · Cited by 18
“For example, FL is used to predict future hospitalizations for patients [25] by considering several attributes such as MRI analysis of subcortical brain data [26], extracting clinical notes [27], classifying EEGs [28], mortality prediction and hospital stay time prediction [29], mortality…”
MethodPersonalized Federated Deep Learning for Pain Estimation From Face ImagesPreprint
Rudovic, O. O., Tobis, N., Kaltwang, S., Schuller, B. W., Rueckert, D., Cohn, J. F., & Picard, R. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.04800 · Cited by 27
“Further evidence of benefits of leveraging FL from the health records under data-privacy constraints can be found in recently published works [6, 9, 10, 30].”
Federated Learning for Privacy-Preserving Data Access
Śmietanka, M., Pithadia, H., & Treleaven, P. C. (2020) · International Journal of Data Science and Big Data Analytics · doi:10.2139/ssrn.3696609 · Cited by 16
Personalized Federated Learning for ECG Classification Based on Feature Alignment
Tang, R., Luo, J., Qian, J., & Jin, J. (2021) · Security and Communication Networks · doi:10.1155/2021/6217601 · Cited by 16
Privacy protected internet of unmanned aerial vehicles for disastrous site identification
Khullar, V., & Singh, H. P. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7040 · Cited by 16
“Brisimi et al. 13 used sparse SVM classification to categorize huge amounts of data in a multi-institutional setting while maintaining dataprivacy.”
BackgroundDecentralized Federated Learning for Nonintrusive Load Monitoring in Smart Energy Communities
Giuseppi, A., Manfredi, S., Menegatti, D., Pietrabissa, A., & Poli, C. (2022) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med54222.2022.9837291 · Cited by 17
Zhang, X., Fang, M., Liu, Z., Yang, H., Liu, J., & Zhu, Z. (2022) · ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (MobiHoc) · doi:10.1145/3492866.3549723 · Cited by 16
Dimolianis, M., Kalogeras, D., Kostopoulos, N., & Maglaris, V. (2022) · IEEE International Conference on Cloud Networking · doi:10.1109/cloudnet55617.2022.9978815 · Cited by 16
“FL has been proposed for various use cases like word prediction [2], healthcare applications [3] and image recognition [4].”
BackgroundChallenges and Trends in Federated Learning for Well-being and Healthcare
Campanile, L., Marrone, S., Marulli, F., & Verde, L. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.09.170 · Cited by 16
Lee, D. Y., Choi, B., Kim, C., Fridgeirsson, E. A., Reps, J., Kim, M.-S., Kim, J., Jang, J., Rhee, S. Y., Seo, W., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/46165 · Cited by 16
Archetti, A., Ieva, F., & Matteucci, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.07.036 · Cited by 16
“Leveraging distributed data while maintaining data privacy, FL models have outperformed traditional statistical methods in predicting outcomes based on medical data [57]–[59].”
MethodFedST: Federated Style Transfer Learning for Non-IID Image Segmentation
Ma, B., Yin, X., Tan, J., Chen, Y., Huang, H., Wang, H. H., Xue, W., & Ban, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28199 · Cited by 16
“…learning across multiple clients without infringing privacy (Jiang et al. 2020), which enables it to be used in many fields where data security is of importance, such as medical data analysis (Brisimi et al. 2018; Liu et al. 2021) and wireless communication (Niknam, Dhillon, and Reed 2020).”
BackgroundImproving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Xie, C., Chen, P., Li, Q., Nourian, A., Zhang, C., & Li, B. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00029 · Cited by 16
“Federated learning (FL) has enabled large-scale training with data privacy guarantees on distributed data for different applications [80, 8, 31, 79, 77].”
BackgroundMultidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0
Abbas, T., Fatima, A., Shahzad, T., Alharbi, M., Khan, M. A., & Ahmed, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-68919-1 · Cited by 20
“Hospitalization prediction was created by FL of predictive models in electronic health records (EHR) utilizing a decentralized optimization methodology 21,22 .”
MethodApplication of privacy protection technology to healthcare big data
Shin, H., Ryu, K., Kim, J., & Lee, S. (2024) · Digital Health · doi:10.1177/20552076241282242 · Cited by 17
Ranney, R. M., Bing‐Canar, H., Paltell, K. C., Tran, J. K., Berenz, E. C., & Vujanovic, A. A. (2020) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2020.110269 · Cited by 20
Budget Distributed Support Vector Machine for Non-ID Federated Learning Scenarios
Navia-Vázquez, A., Díaz-Morales, R., & Fernández-Díaz, M. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3539734 · Cited by 17
“…only and also require the exchange of local models/solutions, such as the Alternating Direction Method of Multipliers [15, 16], Distributed Dual Ascend [20], Distributed Block Minimization [36], the resilient approach presented in [56, 57, 59], or the approaches described in [2, 4, 19, 21, 45, 55].”
FedEE: A Federated Graph Learning Solution for Extended Enterprise Collaboration
Xie, Z., Huang, Y., Yu, D., Parizi, R. M., Zheng, Y., & Pang, J. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3216238 · Cited by 16
“Commencing with the first FL framework articulated by Google in 2016, many works are incorporated into this framework into various industry application scenarios, such as Google’s Gboard, health AI [13], [14] and industrial IoT [15].”
MethodFederated Learning of User Authentication ModelsPreprint
Hosseini, H., Yun, S., Park, H., Louizos, C., Soriaga, J. B., & Welling, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.04618 · Cited by 19
“FL has been used in a variety of applications, such as mobile keyboard prediction (Hard et al., 2018; Yang et al., 2018), keyword detection (Leroy et al., 2019), medical applications (Brisimi et al., 2018) and wireless communications (Niknam et al., 2019).”
Landman, T., & Nissim, N. (2025) · IEEE Access · doi:10.1109/access.2025.3540955 · Cited by 19
Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning
Enthoven, D., & Al-Ars, Z. (2022) · International Conference on Internet of Things: Systems, Management and Security (IOTSMS) · doi:10.1109/iotsms58070.2022.10062088 · Cited by 28
Jiang, D., Tan, C., Peng, J., Chen, C., Wu, X., Zhao, W., Song, Y., Tong, Y., Liu, C., Xu, Q., et al. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3447687 · Cited by 20
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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