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.
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
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?
Xie et al. [485] designed a variant of local SGD in which clients locally perform Adagrad.
“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]
The Cluster Primal Dual Splitting section (page unavailable) says the authors propose cPDS for decentralized sparse-SVM learning, and Algorithm 1 specifies primal and dual updates. That is a different method from the attributed local-SGD variant with client-side Adagrad; searches for those terms found no supporting passage. (LLM, conf=0.87)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredFederated learning supports predictive modeling from diverse sources and can give clinicians more insight into earlier treatment risks and benefits.
“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]
The Abstract (Objective) and Conclusions describe a federated model that uses distributed EHR data to predict future hospitalizations. The Introduction (Health application) links prediction and early prevention, but the paper does not assess the risks and benefits of treating patients earlier or show that its model gives clinicians insight into that trade-off. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredFederated learning has been applied in electronic health records to represent and find clinically similar patients, predict hospitalisations due to cardiac events, mortality and ICU stay time; in medical imaging for whole-brain segmentation in MRI and brain tumour segmentation; for fMRI classification to find disease-related biomarkers; and suggested as a promising approach for COVID-19.
“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 federated learning, data governance remains local, reducing privacy concerns, and only model weights or gradients are communicated between client-sites and the federated server.
“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]
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].
“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]
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.
“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]
DLG obtained the private training data at the inference phase.
“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]
The proposed model in [35] converges faster and with less communication overhead compared to an alternative distributed algorithm.
“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]
An FL-based approach predicts future hospitalizations for patients diagnosed with heart diseases using historic EHRs and a global SVM classifier without disclosing the private dataset.
“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]
Brisimi et al. (2018) use EMRs to evaluate whether a patient with heart disease will be hospitalized based on FL algorithm called cluster Primal Dual Splitting (cPDS).
“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)
Brisimi et al. (2018) need more iterations for convergence.
“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
Brisimi et al. (2018) used Cluster Primal Dual Splitting algorithm to predict future hospitalizations for patients.
“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
Brisimi et al. (2018) yielded classifiers using relatively few features.
“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.
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · cited 2× · see citing works
“We consider SVMs because they are effective classifiers [14] and perform well in predicting hospitalizations.”
MethodBertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,969 · see citing works
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× · see citing works
“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 · see citing works
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× · see citing works
“[ 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 · see citing works
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856 · see citing works
Weik, M. H. (2000) · Springer eBooks · doi:10.1007/1-4020-0613-6_12436 · Cited by 2,584 · see citing works
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 · see citing works
Statistical Learning with Sparsity: The Lasso and Generalizations
Hastie, T., Tibshirani, R., & Wainwright, M. (2015) · International Statistical Review · doi:10.1111/insr.12167 · Cited by 2,489 · cited 1× · see citing works
“[6] Results in the literature suggest that sparse classifiers (i.e., those that rely on few features), have strong predictive power and generalize well out-of-sample,[7,8] providing at the same time interpretability in both models and results.”
BackgroundIncremental Subgradient Methods for Nondifferentiable Optimization
Nedić, A., & Bertsekas, D. P. (2001) · SIAM Journal on Optimization · doi:10.1137/s1052623499362111 · Cited by 590 · see citing works
Yu, W., Liu, T., Valdez, R. A., GWINN, M. L., & Khoury, M. J. (2010) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-10-16 · Cited by 513 · cited 1× · see citing works
“…supervised classification problems and solved using Support Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to…”
MethodKhandoker, A. H., Palaniswami, M. S., & Karmakar, C. K. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2004495 · Cited by 364 · cited 1× · see citing works
“[6] Results in the literature suggest that sparse classifiers (i.e., those that rely on few features), have strong predictive power and generalize well out-of-sample,[7,8] providing at the same time interpretability in both models and results.”
BackgroundDifferentially Private Distributed Optimization
Huang, Z., Mitra, S., & Vaidya, N. (2015) · Proceedings of the 16th International Conference on Distributed Computing and Networking · doi:10.1145/2684464.2684480 · Cited by 266 · see citing works
Classification Of Diabetes Disease Using Support Vector Machine
Kumari, V., & Chitra, R. (2014) · Cited by 219 · see citing works
Statnikov, A., Tsamardinos, I., Dosbayev, Y., & Aliferis, C. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2005.05.002 · Cited by 199 · cited 1× · see citing works
“…Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea syndrome .”
BackgroundDifferentially Private Distributed Convex Optimization via Functional Perturbation
Nozari, E., Tallapragada, P., & Cortés, J. (2016) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2016.2614100 · Cited by 191 · cited 1× · see citing works
“[ 9–11] Two well-known examples of privacy breaches are the Netflix Prize and the Massachusetts Group Insurance Commission (GIC) medical records database.”
BackgroundSon, Y., Kim, H., Kim, E., Choi, S., & Lee, S. (2010) · Healthcare Informatics Research · doi:10.4258/hir.2010.16.4.253 · Cited by 184 · cited 1× · see citing works
“…Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea syndrome .”
BackgroundPrediction of hospitalization due to heart diseases by supervised learning methods
Dai, W., Brisimi, T. S., Adams, W. G., Mela, T., Saligrama, V., & Paschalidis, I. C. (2014) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2014.10.002 · Cited by 143 · cited 2× · see citing works
“…Support Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea…”
BackgroundSelf-citationNationwide Frequency and Costs of Potentially Preventable Hospitalizations, 2006
Jiang, H., Russo, C. A., & Barrett, M. L. (2009) · Cited by 71 · see citing works
Olshevsky, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4186 · Cited by 71 · cited 1× · see citing works
“[19] The LAC algorithm is an iterative algorithm that takes smalls steps towards the optimal solution at each iteration utilizing a fixed but small step size and is shown to have convergence rate.”
MethodSelf-citationHj, J., Russo, C., & Ml, B. (2006) · Agency for Healthcare Research and Quality (US), Rockville (MD) eBooks · Cited by 14 · see citing works
Xu, T., Brisimi, T. S., Wang, T., Dai, W., & Paschalidis, I. C. (2016) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2016.7798964 · Cited by 9 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [8]Chen R Paschalidis IC Outlier Detection Using Distributionally Robust Optimization under the Wasserstein Metric ArXiv Prepr. ArXiv170602412 2017
Cited by
Sorted by citations
Advances and Open Problems in Federated Learning
Kairouz, P., & McMahan, H. B. (2021) · Foundations and Trends in Machine Learning · doi:10.1561/2200000083 · Cited by 7,772 · see citing works
Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · see citing works
“Federated learning methods have been deployed in practice by major companies [5], [41] and play a critical role in supporting privacy-sensitive applications where training data are distributed at the edge [8], [19].”
MethodThe future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y.-C., Yang, Q., Niyato, D., & Miao, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2986024 · Cited by 2,565 · see citing works
“, to develop predictive models for diagnosis in health AI [25] and to foster collaboration across multiple hospitals [26] and Government agencies [27].”
BackgroundSheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69250-1 · Cited by 1,646 · see citing works
“…we acknowledge that the synchronization used in this study (1 epoch per synchronization, i.e., federated round) may be insufficient for data such as electronic health records 28,29 and clinical notes, as well as genomics, where more variance might be present across international institutions.”
A review of applications in federated learning
Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559 · see citing works
“In addition, Brisimi et al., (2018) also use EMRs to evaluate whether a patient with”
BackgroundFederated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · see citing works
“Mortality Prediction [37] Autoencoder 5-50 eICU Collaborative Research Database [73] Hospitalizations Prediction [9] SVM 5, 10 Boston Medical Center Preterm-birth Prediction [8] RNN 50 Cerner Health Facts”
BackgroundMethodFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“[114] EHRs management DFL SVM Health users Data center An FL-based distributed binary supervised classification scheme.”
BackgroundMethodFederated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. (2021) · Deakin Research Online (Deakin University) · Cited by 889 · see citing works
Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works
“Governance of data for FL is maintained locally, alleviating privacy concerns, with only model weights or gradients communicated between client sites and the federated server 38,39 .”
BackgroundFederated learning for smart healthcare: A survey
Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827 · see citing works
“Furthermore, an FL-based approach is proposed in [81] to predict hospitalizations for patients diagnosed with heart diseases using their historic EHRs.”
BackgroundAbdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
“Others find FL great fit in the healthcare domain [10], [11], [65]–[71], where patient privacy is balanced with ML by keeping patient data on-premise in”
BackgroundZhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
“the perceiving and learning ability of AIoT devices, such as connected vehicles for autonomous driving [199] and wearable devices for health monitoring [197], [222].”
BackgroundAlgorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750 · see citing works
“…learning has been applied to a variety of clinical settings to overcome data-interoperability standards that would usually prohibit sensitive health data from being shared and to tackle low-data regimes of clinical machine-learning tasks, for example for the prediction of rare diseases 98,206–220 .”
BackgroundSelf-citationFederated learning for healthcare: Systematic review and architecture proposal
Antunes, R. S., André da Costa, C., Küderle, A., Yari, I. A., & Eskofier, B. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501813 · Cited by 595 · see citing works
“[44], which explores the design of a distributed architecture to solve large-scale sparse support vector machine (SVM) models.”
BackgroundMethodPrivacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581 · see citing works
Federated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556 · see citing works
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment
Haendel, M. A., Chute, C. G., Bennett, T. D., Eichmann, D. A., Guinney, J., Kibbe, W. A., Payne, P. R. O., Pfaff, E. R., Robinson, P. N., Saltz, J. H., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa196 · Cited by 553 · see citing works
“19 While these methods show great promise, we have chosen not to pursue this approach at this time to avoid adding complexity to an already ambitious project.”
BackgroundBibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024) · Environmental Science and Ecotechnology · doi:10.1016/j.ese.2023.100330 · Cited by 536 · see citing works
“These add to transduction learning, multitasking learning [73], federated learning [74], transfer learning [75], and few-shot learning [76].”
BackgroundSee through Gradients: Image Batch Recovery via GradInversion
Yin, H., Mallya, A., Vahdat, A., Álvarez, J. M., Kautz, J., & Molchanov, P. A. (2021) · 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/cvpr46437.2021.01607 · Cited by 482 · see citing works
Self-Balancing Federated Learning With Global Imbalanced Data in Mobile Systems
Duan, M., Liu, D., Chen, X., Liu, R., Tan, Y., & Liang, L. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3009406 · Cited by 434 · see citing works
“[24] propose cPDS to solve the problem of predicting hospitalizations due to heart diseases, the privacy of participant’s data is preserved with the help of the distributed training method.”
Shifting machine learning for healthcare from development to deployment and from models to data
Zhang, A., Xing, L., Zou, J., & Wu, J. C. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00898-y · Cited by 420 · see citing works
“It has been used to leverage EHRs to train models to predict hospitalization due to heart disease 72 , to promote the development of ‘digital twins’ or ‘Google for patients’ 73 , and to develop a Coronavirus disease 2019 (COVID-19) chest-CT lesion segmenter 74 .”
MethodAttack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works
“Several current applications of FL include text prediction in mobile device messaging [1, 2, 3, 4, 5], speech recognition [6], face recognition for device access [7, 8], and maintaining decentralized predictive models across health organizations [9, 10, 11].”
BackgroundRahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399 · see citing works
“FL in healthcare is used to detect similarity in the patient in [106] and is used for predictive modeling as detailed in [107, 108].”
MethodThapa, C., & Camtepe, S. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104130 · Cited by 424 · see citing works
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
Ye, D., Yu, R., Pan, M., & Han, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2968399 · Cited by 381 · see citing works
Radiomics: from qualitative to quantitative imaging
Rogers, W., Seetha, S. T., Refaee, T., Lieverse, R. I. Y., Granzier, R. W. Y., Ibrahim, A., Keek, S. A., Sanduleanu, S., Primakov, S. P., Beuque, M. P. L., et al. (2020) · British Journal of Radiology · doi:10.1259/bjr.20190948 · Cited by 378 · see citing works
Homomorphic Encryption-Based Privacy-Preserving Federated Learning in IoT-Enabled Healthcare System
Zhang, L., Xu, J., Vijayakumar, P., Sharma, P. K., & Ghosh, U. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3185327 · Cited by 376 · see citing works
From distributed machine learning to federated learning: a survey
Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01664-x · Cited by 374 · see citing works
“, healthcare [18], computer visions [86], and industrial groups,”
BackgroundThree Approaches for Personalization with Applications to Federated LearningPreprint
Mansour, Y., Mohri, M., Ro, J. H., & Suresh, A. T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10619 · Cited by 337 · see citing works
“This global model benefits from having access to client data and can often perform better on several learning problems, including next word prediction (Hard et al., 2018; Yang et al., 2018) and predictive models in health (Brisimi et al., 2018).”
FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning
AbdulRahman, S., Tout, H., Mourad, A., & Talhi, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028742 · Cited by 323 · see citing works
“Once Google invented FL, different related aspects have been advanced in the field of models aggregation [2], [6], [7], applications [8]–[10], privacy [11]–[13], security [14]–[16], etc.”
BackgroundLim, W. Y. B., Ng, J. S., Xiong, Z., Jin, J., Zhang, Y., Niyato, D. T., Leung, C., & Miao, C. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3096076 · Cited by 320 · see citing works
“This has enabled several practical applications, e.g., in the development of next-word-prediction models for text messaging [6], healthcare [7], Unmanned Aerial Vehicles (UAV) sensing [8], and mobile edge computing [9].”
MethodFederated Learning in Smart City Sensing: Challenges and Opportunities
Jiang, J. C., Kantarcı, B., Oktuğ, S., & Soyata, T. (2020) · Sensors · doi:10.3390/s20216230 · Cited by 322 · see citing works
“The study in [174] used electronic health records to train an AI model for predicting cardiac events.”
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
“Its potential applications span diverse domains, such as healthcare [25], [52], [53], natural language processing (NLP) [54], transportation [55], [56], and finance [57].”
BackgroundA knowledge graph to interpret clinical proteomics data
Santos, A., Colaço, A. R., Nielsen, A. B., Niu, L., Strauss, M. T., Geyer, P. E., Coscia, F., Albrechtsen, N. J. W., Mundt, F. O., Jensen, L. J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01145-6 · Cited by 345 · see citing works
“New approaches, such as differential privacy and federated learning 70,71 , would allow researchers to use the CKG to train models iteratively across institutions without direct access to the sensitive data (Fig.”
BackgroundFederated learning in a medical context: A systematic literature review
Pfitzner, B., Steckhan, N., & Arnrich, B. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3412357 · Cited by 288 · see citing works
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279 · see citing works
“Brisimi et al. (2018) adopted FL to train a predictive model and solve a support vector machine problem for analysis of electronic health record (EHR) data.”
Federated learning for COVID-19 screening from Chest X-ray images
Feki, I., Ammar, S., Kessentini, Y., & Muhammad, K. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107330 · Cited by 278 · see citing works
SAFELearn: Secure Aggregation for private FEderated Learning
Fereidooni, H., Marchal, S., Miettinen, M., Mirhoseini, A., Möllering, H., Nguyen, T. D., Rieger, P., Sadeghi, A., Schneider, T. H., Yalame, H., et al. (2021) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw53761.2021.00017 · Cited by 281 · see citing works
“data [6], communication between vehicles [7], and intrusion detection systems [8].”
BackgroundFederated learning for computational pathology on gigapixel whole slide images
Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works
“These include large-scale multicenter studies of genomics (Mandl et al., 2020; Rehm, 2017; Jagadeesh et al., 2017), electronic health records (Brisimi et al., 2018; Choud-hury et al., 2019a,b), or wearable health devices (Chen et al., 2020).”
BackgroundSelf-citationDistributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach
Jithish, J., Alangot, B., Mahalingam, N., & Yeo, K. S. (2023) · IEEE Access · doi:10.1109/access.2023.3237554 · Cited by 258 · see citing works
“[25] utilised FL to train a Support Vector Machine (SVM) classifier to predict cardiovascular disease hospitalisations.”
MethodRehman, A., Abbas, S., Khan, M. A., Ghazal, T. M., Khan, M. A., & Mosavi, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106019 · Cited by 283 · see citing works
“[37] forecasted timely treatments for patients with cardiovascular illnesses by utilizing FL to solve dispersed sparse Support Vector Machine difficulties.”
BackgroundSteele, A. J., Cakiroglu, S. A., Shah, A. D., Denaxas, S., Hemingway, H., & Luscombe, N. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202344 · Cited by 241 · see citing works
Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101419 · Cited by 255 · see citing works
“…(MRI) for brain tumor segmentation 7 ; chest X-rayforCOVID-19clinicaloutcomeprediction 8 ;electronicmed-ical records for predicting hospitalization 9 ; colored photographs, such as skin photographs in skin lesion diagnostics 10 and retinal fundus photographs 11 ; and histology slides, such as in…”
BackgroundHierarchical Incentive Mechanism Design for Federated Machine Learning in Mobile Networks
Lim, W. Y. B., Xiong, Z.-H., Miao, C., Niyato, D., Yang, Q., Leung, C., & Poor, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2985694 · Cited by 181 · see citing works
“For example, Nvidia [30] has recently proposed an FL approach for healthcare analytics using patients’ data from multiple collab-orating hospitals [31], i.e., model owners.”
MethodFetchSGD: Communication-Efficient Federated Learning with SketchingPreprint
Rothchild, D., Panda, A., Ullah, E., Ivkin, N. P., Stoica, I. G., Braverman, V., Gonzalez, J. E., & Arora, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07682 · Cited by 234 · see citing works
“These devices have data that is useful for training a variety of models – for text prediction, speech modeling, facial recognition, document identification, and other tasks (Shi et al., 2016; Brisimi et al., 2018; Leroy et al., 2019; Tomlinson et al., 2009).”
Fekri, M. N., Grolinger, K., & Mir, S. H. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107669 · Cited by 226 · see citing works
“FL is a burgeoning learning paradigm that has shown promising prospects in various industrial and engineering fields such as health care [12, 13], autonomous vehicle [14], text analysis [15], and image processing [16], to name but a few [17].”
BackgroundFederated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Rani, S., Kataria, A., Kumar, S., & Tiwari, P. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110658 · Cited by 228 · see citing works
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Qu, L., Zhou, Y., Liang, P. P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L., & Rubin, D. L. (2022) · 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/cvpr52688.2022.00982 · Cited by 220 · see citing works
“This has proved especially useful in domains such as healthcare [4, 7, 15, 40], learning from mobile devices [17, 38], s-”
BackgroundPrivacy-first health research with federated learning
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00489-2 · Cited by 226 · see citing works
Prayitno, Shyu, C.-R., Putra, K. T., Chen, H.-C., Tsai, Y.-Y., Hossain, K. S. M. T., Jiang, W., & Shae, Z.-Y. (2021) · Applied Sciences · doi:10.3390/app112311191 · Cited by 210 · see citing works
“…e.g., autism spectrum disorder (ASD) [18], cancer diagnosis [41,43,49], COVID ‐ 19 detection [40,44,48,54], human activity and emotion recognition [20,21,37,42,45], patient hospitalization prediction [52], patient mortality prediction [19,39,46,47,55,56], and sepsis disease diagnosis [51].”
InfluentialFederated Learning for Healthcare Domain - Pipeline, Applications and Challenges
Joshi, M., Pal, A., & Sankarasubbu, M. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3533708 · Cited by 207 · see citing works
“Federated learning of predictive models from federated electronic health records [13] 2018 Framework A novel FL framework is presented that can train predictive models through peer-to-peer cooperation instead of exchanging raw EHR data”
MethodTrustworthy ai: A computational perspective
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261 · see citing works
Review on security of federated learning and its application in healthcare
Li, H., Li, C., Wang, J., Yang, A., Ma, Z., Zhang, Z., & Hua, D. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.02.021 · Cited by 191 · see citing works
Myrzashova, R., Alsamhi, S. H., Shvetsov, A. V., Hawbani, A., & Wei, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263598 · Cited by 178 · see citing works
“Moreover, additional privacy-preserving mechanisms should be integrated into EHR and EMR management systems [114].”
BackgroundHandling privacy-sensitive medical data with federated learning: Challenges and future directions
Aouedi, O., Sacco, A., Piamrat, K., & Marchetto, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185673 · Cited by 165 · see citing works
“In the context of electronic health records, for example, FL helps to represent and find clinically similar patients [34], as well as predict hospitalizations due to cardiac events [35].”
Kumar, P. S., Dwivedi, Y. K., & Anand, A. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10136-6 · Cited by 178 · see citing works
Federated Learning for Edge Computing: A Survey
Brecko, A., Kajáti, E., Koziorek, J., & Zolotová, I. (2022) · Applied Sciences · doi:10.3390/app12189124 · Cited by 162 · see citing works
“Using electronic health records’ data dispersed across various data sources, FL is also used in [120] to forecast hospitalizations for patients with heart disease during a target year.”
MethodPrivacy-Preserving Asynchronous Federated Learning Mechanism for Edge Network Computing
Lu, X., Liao, Y., Lio, P., & Hui, P. (2020) · IEEE Access · doi:10.1109/access.2020.2978082 · Cited by 159 · see citing works
“In addition, researchers began to implement the federated learning system on vehicle-to-vehicle communications [19], medical applications [20], and in other areas.”
BackgroundApplications of Federated Learning; Taxonomy, Challenges, and Research Trends
Shaheen, M., Farooq, M. S., Umer, T., & Kim, B.-S. (2022) · Electronics · doi:10.3390/electronics11040670 · Cited by 157 · see citing works
Concepts and applications of digital twins in healthcare and medicine
Zhang, K., Zhou, H.-Y., Baptista‐Hon, D. T., Gao, Y., Liu, X., Oermann, E. K., Xu, S. H., Jin, S., Zhang, J., Sun, Z., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101028 · Cited by 170 · see citing works
“103 Cross-silo FL is also an increasingly attractive solution for predicting heart disease hospitalizations through electronic health records, 104 while cross-device FL has been used to handle continuous health data from wearable devices to deliver personalized health insights.”
BackgroundLocal and Central Differential Privacy for Robustness and Privacy in Federated Learning
Naseri, M., Hayes, J., & De Cristofaro, E. (2022) · Proceedings 2022 Network and Distributed System Security Symposium · doi:10.14722/ndss.2022.23054 · Cited by 188 · see citing works
“, Google’s predictive keyboard [38, 113], voice assistants [6], emoji prediction [91], healthcare [10, 98], etc.”
BackgroundFederated learning in smart cities: Privacy and security survey
Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 160 · see citing works
Rodríguez-Barroso, N., Stipcich, G., Jiménez-López, D., Ruiz-Millán, J. A., Martínez-Cámara, E., González-Seco, G., Luzón, M. V., Veganzones, M. A., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.009 · Cited by 148 · see citing works
Challenges, Applications and Design Aspects of Federated Learning: A Survey
Rahman, K. M. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., Islam, A. K. M. M., Mukta, M. S. H., & Islam, A. K. M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3111118 · Cited by 147 · see citing works
“Data partitioning schemes, namely HFL [74], VFL [75]–[78], and hybrid FL [79], [80], as well as centralized [11], [81] and decentralized [82] designs for communication architecture and cross-silo [83] versus cross-device [84], [85] FL for the federation scale, are the other challenges and…”
BackgroundMethodShort-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach
Savi, M., & Olivadese, F. (2021) · IEEE Access · doi:10.1109/access.2021.3094089 · Cited by 147 · see citing works
“Federated Learning unlocks the full potential of Edge Computing for machine learning purposes, and has been already successfully adopted in some application domains [13], such as human-computer interaction [14], language modeling [15], healthcare [16] [17] [18], transportation [19] [20] and Industry 4.”
BackgroundManias, D. M., & Shami, A. A. (2021) · IEEE Network · doi:10.1109/mnet.011.2000552 · Cited by 142 · see citing works
“One such privacy-sensitive application which has already begun to implement FL is the intelligent healthcare sector [8] .”
BackgroundHe, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141 · see citing works
Federated machine learning for privacy preserving, collective supply chain risk prediction
Ge, Z., Kong, L., & Brintrup, A. M. (2023) · International Journal of Production Research · doi:10.1080/00207543.2022.2164628 · Cited by 155 · see citing works
“Non-mobile device applications included cross-institutional medical diagnosis (Sheller et al. 2020), forecasting hospitalisations in the USA (Brisimi et al. 2018; Dayan et al.2021), credit risk (Kawa et al. 2019), fraud detection (Yang et al. 2019; Li et al. 2019), energy demand forecasting (Briggs, Fan, and Andras 20…”
BackgroundAuditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Li, Z., Zhang, J., Liu, L., & Liu, J. (2022) · 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/cvpr52688.2022.00989 · Cited by 138 · see citing works
“language understanding [16,20], medical research [4,8,41], and financial services [32, 50].”
BackgroundZerka, F., Barakat, S., Walsh, S., Bogowicz, M., Leijenaar, R. T. H., Jochems, A., Miraglio, B., Townend, D. M. R., & Lambin, P. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00047 · Cited by 136 · see citing works
Federated Learning on Clinical Benchmark Data: Performance Assessment
Lee, G. H., & Shin, S.-Y. (2020) · Journal of Medical Internet Research · doi:10.2196/20891 · Cited by 136 · see citing works
“In particular, studies have been conducted using electronic medical records and brain tumor data [23-25].”
BackgroundFederated Continual Learning via Knowledge Fusion: A Survey
Yang, X., Yu, H., Gao, X., Wang, H., Zhang, J., & Li, T. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3363240 · Cited by 137 · see citing works
“After several years of development, FL has produced promising results, particularly in industries that handle sensitive data such as finance, healthcare, electronic communication and government affairs [6], [7], [8].”
BackgroundGangwal, A., Ansari, A., Ahmad, I., Azad, A. K., & Sulaiman, W. M. A. W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108734 · Cited by 137 · see citing works
Bao, G., & Guo, P. (2022) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-022-00377-4 · Cited by 131 · see citing works
“More applications are landing in many other industries, such as intrusion detection in the Industrial Internet of Things (IIoT) [4], AI diagnosis in the healthcare industry [5], and traffic analysis in smart transportation [6].”
BackgroundHughes, J. W., Olgin, J. E., Avram, R., Abreau, S. A., Sittler, T., Radia, K., Hsia, H. H., Walters, T. E., Lee, B. K., Gonzalez, J. E., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.2746 · Cited by 130 · see citing works
Recent methodological advances in federated learning for healthcare
Zhang, F., Kreuter, D., Chen, Y., Dittmer, S., Tull, S., Shadbahr, T., Schut, M. C., Asselbergs, F. W., Kar, S., Sivapalaratnam, S., et al. (2024) · Patterns · doi:10.1016/j.patter.2024.101006 · Cited by 142 · see citing works
Challenges and future directions of secure federated learning: a survey
Zhang, K., Song, X., Zhang, C., & Yu, S. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-021-0598-z · Cited by 127 · see citing works
Li, M., Xu, P., Hu, J., Tang, Z., & Yang, G. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103497 · Cited by 143 · see citing works
A comprehensive survey on federated learning techniques for healthcare informatics
Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126 · see citing works
Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
Can, Y. S., & Ersoy, C. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3428152 · Cited by 123 · see citing works
“By using FL, they could share their model with other hospitals (see example applications of FL in electronic health records [20] and activity data [36] in this scenario) and the global model could gain new insights from other data while preserving privacy (see Figure 2(b)).”
BackgroundMethodFedSoft: Soft Clustered Federated Learning with Proximal Local Updating
Ruan, Y., & Joe‐Wong, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20785 · Cited by 122 · see citing works
Abbas, S., Issa, G. F., Fatima, A., Abbas, T., Ghazal, T. M., Ahmad, M., Yeun, C. Y., & Khan, M. A. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/2599161 · Cited by 122 · see citing works
“Te FL of predictive models in electronic health records (EHR) [34] proposed a decentralized optimization framework for prediction in hospitalization.”
BackgroundPlant Genotype to Phenotype Prediction Using Machine Learning
Danilevicz, M. F., Gill, M., Anderson, R., Batley, J., Bennamoun, M., Bayer, P. E., & Edwards, D. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.822173 · Cited by 127 · see citing works
“Federated learning has seen increasing application in digital health, where data sensitivity is a major issue (Brisimi et al., 2018; Lee et al., 2018; Rieke et al., 2020).”
BackgroundA Blockchain‐Based Federated Learning Method for Smart Healthcare
Chang, Y., Fang, C., & Sun, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/4376418 · Cited by 119 · see citing works
“Brisimi et al. [10] predicted hospitalizations for patients with heart diseases by solving distributed sparse Support Vector Machine problems using FL. Xu et al. [11] gave a review for FL methods and pointed out the implications and potentials of FL in healthcare particularly.”
Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images
Yao, Z., Wang, H., Yan, W., Wang, Z. Q., Zhang, W., Wang, Z., & Zhang, G. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110934 · Cited by 131 · see citing works
Zero Knowledge Clustering Based Adversarial Mitigation in Heterogeneous Federated Learning
Chen, Z., Pu, T., Liao, W., & Yu, W. (2020) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2020.3002796 · Cited by 115 · see citing works
Ullah, F., Nadeem, M., Abrar, M. Y. B. T., Amin, F., Salam, A., & Khan, S. (2023) · Mathematics · doi:10.3390/math11194189 · Cited by 114 · see citing works
“3 of 27 Since its inception, FL has largely been applied to such tasks, i.e., segmentation, reconstruction, and classification , with significantly reliable performance and results [14,15]. centralized controller for the refinement of the overall model’s performance.”
Reviewing Federated Machine Learning and Its Use in Diseases Prediction
Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114 · see citing works
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Liu, X., Deng, Y., Nallanathan, A., & Bennis, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3330910 · Cited by 115 · see citing works
“…training predictive models for human trajectory/behavior by mobile devices [33], automatically learning users’ behavior patterns through smart home IoT [34], and enabling collaborative diagnosis in health artificial intelligence (AI) among multiple hospitals and government agencies [35], [36], [37].”
BackgroundPrivacy-Preserving Artificial Intelligence Techniques in Biomedicine
Torkzadehmahani, R., Nasirigerdeh, R., Blumenthal, D. B., Kacprowski, T., List, M., Matschinske, J., Späth, J., Wenke, N. K., & Baumbach, J. (2022) · Methods of Information in Medicine · doi:10.1055/s-0041-1740630 · Cited by 117 · see citing works
“[112] presented a federated soft-margin support vector machine (sSVM) for distributed electronic health records.”
BackgroundFederated Graph Machine Learning
Fu, X., Zhang, B., Dong, Y., Chen, C., & Li, J. (2022) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3575637.3575644 · Cited by 129 · see citing works
“Several papers deal with cross-hospital links either by reconstructing cross-hospital connections [88] or by transmitting parameters with nearby hospitals [69; 70; 5].”
BackgroundFederated learning: Applications, challenges and future directions
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-220006 · Cited by 117 · see citing works
“Other classification tasks studied include emotion recognition and human activity [98–100] and prediction of patient hospitalization [101,102].”
Adaptive Privacy-Preserving Federated Learning for Fault Diagnosis in Internet of Ships
Zhang, Z., Guan, C., Chen, H., Yang, X., Gong, W., & Yang, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3115817 · Cited by 107 · see citing works
Federated learning with hyperparameter-based clustering for electrical load forecasting
Gholizadeh, N., & Musı́lek, P. (2021) · Internet of Things · doi:10.1016/j.iot.2021.100470 · Cited by 104 · see citing works
FedSim: Similarity guided model aggregation for Federated Learning
Palihawadana, C., Wiratunga, N., Wijekoon, A., & Kalutarage, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.141 · Cited by 102 · see citing works
“Here typical applications include those in healthcare and banking, where a small number of institutions have a business incentive to train a shared model [8, 9, 10, 11, 12, 13].”
BackgroundA Survey on Securing Federated Learning: Analysis of Applications, Attacks, Challenges, and Trends
Neto, H. N. C., Hribar, J., Dusparić, I., Mattos, D. M. F., & Fernandes, N. C. (2023) · IEEE Access · doi:10.1109/access.2023.3269980 · Cited by 108 · see citing works
“As FL enables the use of large amounts of data while preserving user privacy, it has become a popular solution in many areas, such as cyberattack detection, vehicle networks, smart healthcare, and IoT in general [16], [17], [18], [19], [20].”
BackgroundMethodChang, Q., Qu, H. Y., Zhang, Y., Sabuncu, M. R., Chen, C., Zhang, T., & Metaxas, D. (2020) · 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/cvpr42600.2020.01387 · Cited by 101 · see citing works
“Recently, FL attracts more attention as data privacy becomes a concern for users [14, 27, 7, 19].”
BackgroundRoFL: Robustness of Secure Federated Learning
Lycklama, H., Burkhalter, L., Viand, A., Küchler, N., & Hithnawi, A. (2023) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46215.2023.10179400 · Cited by 114 · see citing works
“Federated Learning (FL) is a prominent form of collaborative ML that has recently emerged and is already being used in practice to train models for a variety of privacy-sensitive applications [16, 17, 68, 70, 74, 75].”
BackgroundDecentralised Learning in Federated Deployment Environments
Bellavista, P., Foschini, L., & Mora, A. (2021) · ACM Computing Surveys · doi:10.1145/3429252 · Cited by 101 · see citing works
“Other clinical learning tasks include prediction of prolonged length of stay and in-hospital mortality [96], prediction of hospitalisations for cardiac events [15], or gaining insights about brain diseases [104].”
Background
- 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 · 2012: 1
- 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 › Computer Science › Computer Networks and Communications › 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
