The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“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]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 101–200 of 994
Khan, T. A., Fatima, A., Shahzad, T., Rahman, A., Alissa, K. A., Ghazal, T. M., Al-Sakhnini, M. M., Abbas, S., Khan, M. A., & Ahmed, A. (2023) · IEEE Access · doi:10.1109/access.2023.3266156 · Cited by 100
“been developed to make people’s life easier [40].”
BackgroundA Review of Blockchain-Based Secure Sharing of Healthcare Data
Peng, X., Zhang, X., Wang, L. Y., Liu, W., & Peng, S. (2022) · Applied Sciences · doi:10.3390/app12157912 · Cited by 107
“The privacy of healthcare data prevents data-driven machine learning from working [38].”
BackgroundYaqoob, M. M., Nazir, M., Khan, M. A., Qureshi, S., & Al-Rasheed, A. (2023) · Applied Sciences · doi:10.3390/app13031911 · Cited by 96
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Blanco-Justicia, A., Sánchez, D., Domingo‐Ferrer, J., & Muralidhar, K. (2022) · ACM Computing Surveys · doi:10.1145/3547139 · Cited by 101
“However, FL is employed in many occasions in much smaller configurations, for example in medical applications [3]; unfortunately, resorting to DP-FL to address the privacy requirements of such configurations can lead to unacceptable accuracy loss.”
BackgroundTackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction
Leslie, D. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.4bb9d7a7 · Cited by 103
Gradient Inversion with Generative Image PriorPreprint
Jeon, J., Kim, J., Lee, K., Oh, S., & Ok, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14962 · Cited by 97
“Federated learning (FL) is an emerging framework for distributed learning, where central server aggregates model updates, rather than user data, from end users [5, 17].”
BackgroundAutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
Kim, Y. G., & Wu, C.-J. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480129 · Cited by 99
Hauschild, A.-C., Lemanczyk, M., Matschinske, J., Frisch, T., Zolotareva, O. I., Holzinger, A., Baumbach, J., & Heider, D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac065 · Cited by 91
Predicting Adverse Drug Reactions on Distributed Health Data using Federated Learning.
Choudhury, O., Park, Y., Salonidis, T., Gkoulalas-Divanis, A., Sylla, I., & Das, A. K. (2019) · PubMed Central · Cited by 89
A Survey on federated learning
Li, L., Fan, Y., & Lin, K. (2020) · International Conference on Control and Automation · doi:10.1109/icca51439.2020.9264412 · Cited by 104
“Furthermore, it can be applied to handle information dispersed in various institutions such as hospitals or clinics to solve the problem about detached islands while keeping data on local database [56].”
BackgroundFederated Learning Systems for Healthcare: Perspective and Recent Progress
Kumar, Y., & Singla, R. P. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_6 · Cited by 95
Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90
“[181] Brain Tumor Segmentation FL (DNN) - Dice Score ✓ ✗ Medical Sites (Hospitals) - Enhance the classification accuracy - Enhance the Dice Score - Preserve Model Privacy Intelligent eHealth and Body Area Networks [182] Hospitalization Prediction FL (Sparse SVM) - Communication Cost - Computation Time - AUC ✓ ✗ - Hosp…”
Akkem, Y., Kumar, B. S., & Varanasi, A. (2023) · Indian Journal of Science and Technology · doi:10.17485/ijst/v16i48.2850 · Cited by 102
Li, J., Zhang, C., Zhao, Y., Qiu, W., Chen, Q., & Zhang, X. (2021) · Building Simulation · doi:10.1007/s12273-021-0871-y · Cited by 88
“In the field of medical treatment, Brisimi et al. (2018) proposed a federated learning framework to train a support vector machine model for predicting heart-related hospitalizations accurately.”
BackgroundMethodFederated Learning for Electronic Health Records
Dang, T. K., Xiang, L., Weng, J., & Feng, M. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3514500 · Cited by 98
“[10] proposed an iterative cluster Primal-Dual Splitting (cPDS) algorithm for solving the large-scale soft-margin l1-regularized sparse Support Vector Machine to predict hospitalizations due to cardiac events in FL settings.”
MethodFederated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., & Jiang, J. (2022) · Humanity Driven AI · doi:10.1007/978-3-030-72188-6_6 · Cited by 85
Bebortta, S., Tripathy, S. S., Basheer, S., & Chowdhary, C. L. (2023) · Diagnostics · doi:10.3390/diagnostics13203166 · Cited by 95
“In reference [18], the researchers of a study on patients with conditions probable to necessitate hospitalisation developed a federated-learning-based model.”
BackgroundGawande, M. S., Zade, N., Kumar, P., Gundewar, S. K., Weerarathna, I. N., & Verma, P. (2025) · Molecular Biomedicine · doi:10.1186/s43556-024-00238-3 · Cited by 109
“AI can harmonise disparate sources or formats into a standard, organised model like EHR [59, 60].”
Securing Critical IoT Infrastructures With Blockchain-Supported Federated Learning
Otoum, S., Ridhawi, I. A., & Mouftah, H. T. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3088056 · Cited by 83
Zhou, Z., Li, Y., Ren, X., & Yang, S. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3150579 · Cited by 85
“By providing an effective framework to exploit the naturally generated data while respecting privacy, FL has not only attracted extensive interests from the academia [9], but also been deployed in various practical applications, such as e-health diagnosis [13], [14], fraud detection [15], and recommendation systems [1…”
BackgroundA Differentially Private Federated Learning Model against Poisoning Attacks in Edge Computing
Zhou, J., Wu, N., Wang, Y., Gu, S., Cao, Z., Dong, X., & Choo, K. R. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3168556 · Cited by 93
“Distributed machine learning approaches such as those based on federated learning (FL) [1], [2], [3], [4], [5], for example, can overcome the limitations and have been widely adoptd in e-healthcare systems [6] and VANETs [7].”
BackgroundFederated Networks for Distributed Analysis of Health Data
Hallock, H., Marshall, S., Hoen, P. A. C. ’., Nygård, J. F., Hoorne, B., Fox, C., & Alagaratnam, S. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.712569 · Cited by 84
“…nodes can be not just hospital information systems or institutional databases, but also personal health records from individual citizens, as exemplified by cross-institutional patient matching (22) and identification of risk of cardiac-related hospitalizations (23) from electronic health records.”
BackgroundChen, R., Li, L., Xue, K.-P., Zhang, C., Pan, M., & Fang, Y. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3213766 · Cited by 87
“Its potential has prompted wide applications in various domains such as keyboard predictions [2], physical hazards detection in smart home [3], health event detection [4], and vehicular networks [5].”
BackgroundSelf-citationDecentralized Federated Learning for Electronic Health Records
Lu, S., Zhang, Y., & Wang, Y. (2020) · Conference on Information Sciences and Systems · doi:10.1109/ciss48834.2020.1570617414 · Cited by 81
“To overcome these challenges, federated learning (FL) may be an effective way to increase training efficiency and allow knowledge to be shared without compromising user privacy [1].”
BackgroundSaha, S., Hota, A., Chattopadhyay, A. K., Nag, A., & Nandi, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10766-7 · Cited by 94
A review on federated learning towards image processing
Khokhar, F. A., Shah, J. H., Khan, M. A., Sharif, M. I., Tariq, U., & Kadry, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107818 · Cited by 82
Federated Learning for the Internet-of-Medical-Things: A Survey
Prasad, V. K., Bhattacharya, P., Maru, D., Tanwar, S., Verma, A., Singh, A., Tiwari, A. K., Sharma, R., Alkhayyat, A. H., Țurcanu, F.-E., et al. (2022) · Mathematics · doi:10.3390/math11010151 · Cited by 87
Federated Learning in Health care Using Structured Medical Data
Oh, W., & Nadkarni, G. N. (2023) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.007 · Cited by 88
Nguyen, T., Dakka, M. A., Diakiw, S. M., VerMilyea, M. D., Perugini, M., Hall, J. M. M., & Perugini, D. (2022) · Scientific Reports · doi:10.1038/s41598-022-12833-x · Cited by 86
A federated machine learning approach for order-level risk prediction in Supply Chain Financing
Kong, L., Ge, Z., & Brintrup, A. M. (2023) · International Journal of Production Economics · doi:10.1016/j.ijpe.2023.109095 · Cited by 78
Free-riders in Federated Learning: Attacks and Defenses
Lin, J., Du, M., & Liu, J. (2019) · arXiv · doi:10.48550/arxiv.1911.12560 · Cited by 76
“Federated learning has many practical applications, such as next-word prediction in a virtual keyboard for smartphones [4], prediction of customers’ usage of IoT devices [5] and classi-fication of hospitalizations for cardiac events [6], to achieve a well-trained model.”
BackgroundYamany, W., Moustafa, N., & Turnbull, B. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3130906 · Cited by 87
“The breach of privacy while the learning phase can be minimized significantly within the FL paradigm [8].”
BackgroundYou, L., Liu, S., Chang, Y., & Yuen, C. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3188556 · Cited by 75
“…domain-specific tasks, e.g., in smart home to learn user behaviors [9]; in personal application to assist key-board inputting [10]; in smart manufacturing to detect product defects [11]; in open banking to perform fraud detection [12]; in digital healthcare to predict hospitalization rate [13], etc.”
BackgroundWollenstein-Betech, S., Cassandras, C. G., & Paschalidis, I. C. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104258 · Cited by 77
Self-citationThe Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype
Abdulkareem, M., & Petersen, S. E. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.652669 · Cited by 83
“In addition, researchers, such as in Brisimi et al. (2018), Lee et al. (2018), Rieke et al. (2020), Li et al. (2020d), and Xu et al. (2020d), have proposed the use of federated learning (FL) whereby, rather than participating healthcare institutions hand over healthcare data to AI experts to…”
BackgroundOgundokun, R. O., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2022) · Information · doi:10.3390/info13050263 · Cited by 79
“Brisimi et al. [39] also utilize EMRs to determine if a patient with heart disease will be admitted to the hospital using an FL method known as cluster Primal-Dual Splitting (cPDS).”
BackgroundMethodFedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
Li, L., Duan, M., Liu, D., Zhang, Y., Ren, A., Chen, X., Tan, Y., & Wang, C. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533876 · Cited by 78
“Federated Learning (FL) [1]–[3], famous for its significant contribution on privacy protection [4], is one of the most popular topics on distributed machine learning, widely used in personalized recommendation [5], [6] and clinical disease detection [7].”
BackgroundA survey on federated learning in data mining
Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78
“The federated learning model is leveraged in Brisimi et al. (2018) to predict future hospitalization for cardiac events, based on their medical history as described in the electronic health records.”
MethodFederated Unlearning: Guarantee the Right of Clients to Forget
Wu, L., Guo, S., Wang, J., Hong, Z., Zhang, J., & Ding, Y. (2022) · IEEE Network · doi:10.1109/mnet.001.2200198 · Cited by 73
“Due to its ability to blend clients, federated learning has been widely adopted in many real-world applications, ranging from keyboard prediction [10] to healthcare [11].”
BackgroundTrust-Augmented Deep Reinforcement Learning for Federated Learning Client Selection
Rjoub, G., Wahab, O. A., Bentahar, J., Cohen, R. A., & Bataineh, A. S. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10307-z · Cited by 73
“The applications of FL in medical big data are quite promising (Wahab et al., 2021; Brisimi et al., 2018; Kumar et al., 2021).”
Security and privacy-aware Artificial Intrusion Detection System using Federated Machine Learning
Kumar, K. P. S., Nair, S., Roy, D. G., Rajalingam, B., & Kumar, R. S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107440 · Cited by 73
Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges
Pouriyeh, S., Shahid, O., Parizi, R. M., Sheng, Q. Z., Srivastava, G., Zhao, L., & Nasajpour, M. (2022) · Applied Sciences · doi:10.3390/app12188980 · Cited by 200
Díaz, J. S.-P., & García, Á. L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.011 · Cited by 71
Liu, J., Huang, G., Hyyppa, J. M., Li, J., Gong, X., & Jiang, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120492 · Cited by 79
Shield Against Gradient Leakage Attacks: Adaptive Privacy-Preserving Federated Learning
Hu, J., Wang, Z., Yong-sheng, S., Lin, B., Sun, P., Pang, X., Liu, J., & Ren, K. (2023) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2023.3317870 · Cited by 71
“In light of outstanding effect on privacy protection, FL has been widely applied in many privacy-sensitive scenarios such as typing prediction [2], spoken language understanding [3], [4], medical diagnosis [5], [6], [7], and financial services [8], [9].”
BackgroundA scoping review of the governance of federated learning in healthcare
Eden, R., Chukwudi, I., Bain, C., Barbieri, S., Callaway, L. K., de Jersey, S., George, Y., Gorse, A., Lawley, M., Marendy, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01836-3 · Cited by 88
“In healthcare, FL has been used to predict heart-related hospitalisations 13 , enhance brain MRI analysis 14,15 , and improve early cancer diagnosis accuracy 16 ; bene fi ting a range of diseases 17 without sharing patient data.”
Blockchain-Supported Federated Learning for Trustworthy Vehicular Networks
Otoum, S., Al Ridhawi, I., & Mouftah, H. T. (2020) · GLOBECOM 2020 - 2020 IEEE Global Communications Conference · doi:10.1109/globecom42002.2020.9322159 · Cited by 92
“FL has been employed in various applications including e-healthcare and e-commerce applications [3], [4].”
MethodUllah, F., Srivastava, G., Xiao, H., Ullah, S., Lin, J. C., & Zhao, Y. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3282955 · Cited by 70
“This procedure is repeated multiple times when required [4].”
MethodAdversarial machine learning: a review of methods, tools, and critical industry sectors
Pelekis, S., Koutroubas, T., Blika, A., Berdelis, A., Karakolis, E., Ntanos, C., Spiliotis, E., & Askounis, D. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11147-4 · Cited by 92
FedLab: A Flexible Federated Learning FrameworkPreprint
Zeng, D., Liang, S., Hu, X., Wang, H., & Xu, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.11621 · Cited by 78
“It aims to protect individual data privacy in distributed machine learning applications, especially in finance (Byrd and Polychroniadou, 2020), smart healthcare, (Xu et al., 2021; Brisimi et al., 2018) and edge computing (Jiang et al.”
BackgroundHealthAssistantBot: A Personal Health Assistant for the Italian Language
Polignano, M., Narducci, F., Iovine, A., Musto, C., de Gemmis, M., & Semeraro, G. M. (2020) · IEEE Access · doi:10.1109/access.2020.3000815 · Cited by 68
“This is a very promising solution successfully adopted in the healthcare domain [46] In this way, a user can enter her own symptoms”
BackgroundClinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer
Bitterman, D. S., Miller, T. A., Mak, R. H., & Savova, G. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.01.044 · Cited by 68
The Internet of Federated Things (IoFT)
Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72
“For instance, in the healthcare field, FL is lately being used as a medium of collaboration between hospitals to share patient’s electronic records and other medical data [26, 130, 107, 284].”
BackgroundBlockchain and homomorphic encryption based privacy-preserving model aggregation for medical images
Kumar, R., Kumar, J., Khan, A. A., Zakria, Z., Ali, H., Bernard, C. M., Khan, R. U., & Zeng, S. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102139 · Cited by 66
Peketi, D., Chalavadi, V., Mohan, C. K., & Chen, Y. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191202 · Cited by 66
“It is implemented in a federated framework to find the identical patients from one hospital to another, predictive modeling [14] can analyze predictive models via peer-to-peer collaborations, patient representation learning [15] utilized federated framework for pretraining patient representations,…”
BackgroundMethodBlockchain-Enabled Asynchronous Federated Learning in Edge Computing
Liu, Y., Qu, Y., Xu, C., Hao, Z., & Gu, B. (2021) · Sensors · doi:10.3390/s21103335 · Cited by 65
“…in many areas, e.g., the facial recognition model trained by convolutional neural network (CNN) has been widely applied for smartphone authentication [2], hospital utilizes regression model based on historical medical records to predict patient condition [3], and forthcoming autonomous vehicles.”
MethodA Systematic Survey of Blockchained Federated LearningPreprint
Wang, Z., Hu, Q., Xu, M., Zhuang, Y., Wang, Y., & Cheng, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02182 · Cited by 103
Anomaly detection and defense techniques in federated learning: a comprehensive review
Zhang, C., Yang, S., Mao, L., & Ning, H. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10796-1 · Cited by 73
“Other applications in healthcare include the use of FL to manage digital health records Brisimi et al. (2018) and to detect attacks in physical medical information systems (i.e., those maintaining sensitive information in patient health records Long et al. (2022)).”
Fed-BioMed: A General Open-Source Frontend Framework for Federated Learning in Healthcare
Silva, S., Altmann, A., Gutman, B. A., & Lorenzi, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60548-3_20 · Cited by 71
“Initially conceived for mobile applications [8], federated learning allows to optimize machine learning models on datasets that are distributed across clients, such as multiple clinical centers in healthcare [4].”
BackgroundOptimal Rate Adaption in Federated Learning with Compressed Communications
Cui, L., Su, X., Zhou, Y., & Liu, J. (2022) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom48880.2022.9796982 · Cited by 63
“, for smart devices to predict human trajectories [4] or for healthcare systems to develop predictive models [5].”
BackgroundA Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent
Pu, S., Olshevsky, A., & Paschalidis, I. C. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2021.3126253 · Cited by 67
“Problems in the form of (1) find applications in multiagent target seeking [1], distributed machine learning [2]–[8], and wireless networks [5], [9], [10], among other scenarios.”
BackgroundSelf-citationFederated User Modeling from Hierarchical Information
Liu, Q., Wu, J., Huang, Z., Wang, H., Ning, Y., Chen, M., Chen, E., Yi, J., & Zhou, B. (2023) · ACM Transactions on Information Systems · doi:10.1145/3560485 · Cited by 65
“Some work focuses on combining federated learning with practical applications, such as education [96], news recommendation [69], recommendation systems [101], medical [6, 11], inancial [81].”
BackgroundDeep Federated Learning for IoT-based Decentralized Healthcare Systems
Elayan, H., Aloqaily, M., & Guizani, M. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498820 · Cited by 61
Secure and robust machine learning for healthcare: A survey
Qayyum, A., Qadir, J., Bilal, M., & Al-Fuqaha, A. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3013489 · Cited by 59
“EHR of heart-related diseases is presented in [151].”
BackgroundFailure prediction in production line based on federated learning: an empirical study
Ge, N., Li, G., Zhang, L., & Liu, Y. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01775-2 · Cited by 59
“FL has also been applied to various fields like medical systems Boughorbel et al. (2019b); Brisimi et al. (2018); Huang et al. (2019a); Sheller et al. (2018b), Internet finance Suzumura et al. (2019); Yang et al. (2019b), smart city Liu et al. (2020b); Sama-rakoon et al. (2019); Saputra et al.…”
BackgroundClinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-MakingPreprint
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., & Mann, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.09.084897 · Cited by 59
“New approaches, such as differential privacy and federated learning (Bonawitz et al., 2019; Brisimi et al., 2018), would allow researchers to use the CKG to train models iteratively across institutions without direct access to the sensitive data (Figure 7C).”
BackgroundToward Energy-Efficient Federated Learning Over 5G+ Mobile Devices
Shi, D., Li, L., Chen, R., Prakash, P., Pan, M., & Fang, Y. M. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.003.2100028 · Cited by 68
“Such a combination of 5G+ and FL prompts tremendous successful applications over 5G mobile devices, including keyboard prediction [3], cardiac event prediction [4], financial risk management [5], etc.”
BackgroundSelf-citationYaqoob, M. M., Nazir, M., Yousafzai, A., Khan, M. A., Shaikh, A. A., Algarni, A. D., & Elmannai, H. (2022) · Applied Sciences · doi:10.3390/app122312080 · Cited by 57
“In the paper [21], the authors developed a model based on federated learning for the prediction of hospitalization of health-related disease patients.”
BackgroundPrivacy-preserving Decentralized Learning Framework for Healthcare System
Kasyap, H., & Tripathy, S. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3426474 · Cited by 56
“[5] proposed a decentralized computationally scalable methodology for large-scale machine learning problems in the healthcare domain.”
BackgroundMethodZhao, J., Hu, L., Dong, Y., Huang, L., Weng, S., & Zhang, D. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102459 · Cited by 57
StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing
Huang, A., Liu, Y., Chen, T., Zhou, Y., Sun, Q., Chai, H., & Yang, Q. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3467956 · Cited by 60
Chen, J., Coyner, A. S., Chan, R. V. P., Hartnett, M. E., Moshfeghi, D. M., Owen, L. A., Kalpathy–Cramer, J., Chiang, M. F., & Campbell, J. P. (2021) · Ophthalmology Science · doi:10.1016/j.xops.2021.100079 · Cited by 56
“11 Data-and model-sharing methods including federated 12,13 and distributed 14,15 learning have shown potential in facilitating DL algorithm training without inter-institutional data sharing.”
MethodAn Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies
Enthoven, D., & Al-Ars, Z. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_8 · Cited by 72
Review on Federated Learning for digital transformation in healthcare through big data analytics
Babar, M., Qureshi, B., & Koubaa, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.05.046 · Cited by 55
A comprehensive review of federated learning for COVID‐19 detection
Naz, S., Phan, K. T., & Chen, Y. P. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22777 · Cited by 59
“22,23 A huge communication overhead is reported in other areas of research 62,64 and an effort to reduce communication overhead while preserving data privacy is also reported in Xia et al. 65 4.”
BackgroundEmpowering federated learning techniques for privacy-preserving PV forecasting
Michalakopoulos, V., Sarantinopoulos, E., Sarmas, E., & Marinakis, V. (2024) · Energy Reports · doi:10.1016/j.egyr.2024.08.033 · Cited by 59
Exploring privacy mechanisms and metrics in federated learning
Shenoy, D., Bhat, R., & Prakash, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11170-5 · Cited by 63
Privacy-preserving federated k-means for proactive caching in next generation cellular networks
Liu, Y., Ma, Z., Yan, Z., Wang, Z., Liu, X., & Ma, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.042 · Cited by 57
Privacy-Preserving Federated Learning Model for Healthcare Data
Islam, T. U., Ghasemi, R., & Mohammed, N. (2022) · 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc54503.2022.9720752 · Cited by 56
“Recent works demonstrate the advantages of using FL techniques over traditional centralized ML models for several health applications such as predicting hospital admissions and adverse drug reaction [5], [19].”
MethodPu, S., Olshevsky, A., & Paschalidis, I. C. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975212 · Cited by 58
“…that train such models quickly and reliably in a distributed fashion over networks where the individual processors or GPUs may be scattered across the globe and communicate over an unreliable network, which may suffer from message losses, delays, and asynchrony (see [1], [2], [29], and [33]).”
BackgroundSelf-citationDefinition of a novel federated learning approach to reduce communication costs
Paragliola, G., & Coronato, A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116109 · Cited by 54
Service Delay Minimization for Federated Learning Over Mobile Devices
Chen, R., Shi, D., Qin, X., Liu, D., Pan, M., & Cui, S. R. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242711 · Cited by 54
“FL over mobile devices can provide a variety of services, including Google’s next word prediction [3], cardiac event prediction [4], mobile object detection [5], etc.”
BackgroundSelf-citationDeep learning in industrial machinery: A critical review of bearing fault classification methods
Rehman, A. U., Jiao, W., Jiang, Y., Wei, J., Sohaib, M., Sun, J., Shiju, E., Rehman, K. U., & Chi, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112785 · Cited by 59
Communication-Computation Efficient Secure Aggregation for Federated LearningPreprint
Choi, B., Sohn, J.-Y., Han, D.-J., & Moon, J. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.05433 · Cited by 83
Pocket diagnosis: Secure federated learning against poisoning attack in the cloud
Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K.-K. R., & Deng, R. H. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2021.3090771 · Cited by 50
“Driven by the above issue, federated learning [9], [10], [11] has been proposed as an efficient solution to provide privacy preservations in the cloud, in which a federatedmodel is constructed by aggregating locally-trained models.”
BackgroundGagalova, K., Elizalde, M. A. L., Portales-Casamar, É., & Görges, M. (2020) · JMIR Formative Research · doi:10.2196/17687 · Cited by 53
“A total of 241 articles were identified in the literature search [11,13-19,21-29,31,33-35,37,43,44,47-49,52-264]; the largest number of articles were identified in IEEE Xplore (n=112), followed by MEDLINE (n=95), and Google Scholar (n=71).”
BackgroundCollaborative deep learning framework for fault diagnosis in distributed complex systems
Wang, H., Liu, C., Jiang, D., & Jiang, Z. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107650 · Cited by 54
Multimodal Melanoma Detection with Federated Learning
Agbley, B. L. Y., Li, J., Haq, A. U., Bankas, E. K., Ahmad, S., Agyemang, I. O., Kulevome, D. K. B., Ndiaye, W. D., Cobbinah, B. M., & Latipova, S. (2021) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip53232.2021.9674116 · Cited by 53
“Brisimi et al. [16] presented an FL model that predicts the chances of hospitalizing patients suffering from heart-related diseases.”
BackgroundElastic Aggregation for Federated Optimization
Chen, D., Hu, J., Tan, V. J., Wei, X., & Wu, E. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01173 · Cited by 56
“The server model then benefits from all client data and achieves superior performance, for tasks such as next word prediction [17, 56], emoji prediction [45], decoder models [8], vocabulary estimation [7], low latency vehicle-to-vehicle communication [49] and predictive models in health [6].”
MethodSlashing Communication Traffic in Federated Learning by Transmitting Clustered Model Updates
Cui, L., Su, X., Zhou, Y., & Pan, Y. F. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087262 · Cited by 53
Naseri, M., Hayes, J., & Cristofaro, E. D. (2020) · arXiv (Cornell University) · Cited by 54
A framework for the prediction of earthquake using federated learning
Tehseen, R., Farooq, M. S., & Abid, A. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.540 · Cited by 52
“The FL system has been applied on synchronous training of algorithms ( Lim et al., 2019 ), vehicle-to-vehicle communications ( Samarakoon et al., 2018 ), medical applications ( Brisimi et al., 2018 ), and for earthquake early warning system using smart phones ( Kong et al., 2016 ).”
MethodFirdaus, M., Larasati, H. T., & Rhee, K.-H. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101579 · Cited by 62
Detecting model misconducts in decentralized healthcare federated learning
Kuo, T.-T., & Pham, A. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104658 · Cited by 52
Fida, K., Abbasi, U., Adnan, M., Iqbal, S., & Mohamed, S. E. G. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.102773 · Cited by 59
Fatema, K., Dey, S. K., Anannya, M., Khan, R. T., Rashid, M., Su, C., & Mazumder, R. (2025) · Future Internet · doi:10.3390/fi17060234 · Cited by 51
Kirienko, M., Sollini, M., Ninatti, G., Loiacono, D., Giacomello, E., Gozzi, N., Francesco Amigoni, A., Mainardi, L. T., Lanzi, P. L., & Chiti, A. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05339-7 · Cited by 51
“Brisimi et al. [29] and Wang et al. [38] used clinical data to predict hospitalization risks for cardiac events and re-hospitalization for heart failure, respectively.”
BackgroundAlsharif, M. H., Kannadasan, R., Wei, W., Nisar, K. S., & Abdel‐Aty, A. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101251 · Cited by 54
Javed, L., Anjum, A., Yakubu, B. M., Iqbal, M., Moqurrab, S. A., & Srivastava, G. (2022) · Expert Systems · doi:10.1111/exsy.13131 · Cited by 49
“Then, to give a useful resource for academics, it explains the general answers to the major issues in FL (Brisimi et al., 2021; Xu et al., 2021).”
BackgroundSafeguarding Patient Data-Sharing: Blockchain-Enabled Federated Learning in Medical Diagnostics
Myrzashova, R., Alsamhi, S. H., Hawbani, A., Curry, E., Guizani, M., & Wei, X. (2024) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2024.3409329 · Cited by 53
“Au-thors in [33] implemented an FL system for predictive modelling in a network of hospitals, demonstrating this approach’s efficacy and privacy-preserving capabilities.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
