Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Communication-Efficient Learning of Deep Networks from Decentralized Data

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Distilling the Knowledge in a Neural Network

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Advances and Open Problems in Federated Learning

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Federated Machine Learning: Concept and Applications

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Differential Privacy

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Federated Learning: Strategies for Improving Communication Efficiency

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The future of digital health with federated learning

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Deep Leakage from Gradients

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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data

Sheller, 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

Federated 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

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2019) · arXiv · doi:10.48550/arxiv.1911.06270 · Cited by 1,538 · see citing works

Deep Learning Applications in Medical Image Analysis

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How to backdoor federated learning

Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works

A survey on security and privacy of federated learning

Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., & Srivastava, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.007 · Cited by 1,480 · see citing works

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Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints

Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355 · see citing works

Federated learning of predictive models from federated Electronic Health Records

Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.007 · Cited by 997 · see citing works

New Frontiers in Open Innovation

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An Efficient Framework for Clustered Federated Learning

Ghosh, A., Chung, J., Yin, D., & Ramchandran, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3192506 · Cited by 873 · see citing works

Adaptive Federated Optimization

Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840 · see citing works

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works

Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works

Ensemble Distillation for Robust Model Fusion in Federated LearningPreprint

Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07242 · Cited by 733 · see citing works

Wearable health devices in health care: narrative systematic review

Lu, L., Zhang, J., Xie, Y., Gao, F., Xu, S., Wu, X., & Ye, Z. (2020) · JMIR mHealth and uHealth · doi:10.2196/18907 · Cited by 701 · see citing works

A secure federated transfer learning framework

Liu, Y., Kang, Y., Xing, C., Chen, T., & Yang, Q. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988525 · Cited by 645 · see citing works

Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11723-8_9 · Cited by 609 · see citing works

Federated Learning with Personalization LayersPreprint

Arivazhagan, M. G., Aggarwal, V., Singh, A. K., & Choudhary, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.00818 · Cited by 595 · see citing works

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Hou, N., Li, M., He, L., Xie, B., Wang, L., Zhang, R., Yu, Y., Sun, X., Pan, Z., & Wang, K. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-020-02620-5 · Cited by 580 · see citing works

Privacy-Preserving Federated Brain Tumour Segmentation

Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., et al. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32692-0_16 · Cited by 578 · see citing works

iDLG: Improved Deep Leakage from GradientsPreprint

Zhao, B., Mopuri, K. R., & Bilen, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.02610 · Cited by 575 · see citing works

FedMD: Heterogenous Federated Learning via Model DistillationPreprint

Li, D., & Wang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03581 · Cited by 562 · see citing works

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., & Kim, S.-L. (2018) · arXiv · doi:10.48550/arxiv.1811.11479 · Cited by 465 · see citing works

Attack 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

Think Locally, Act Globally: Federated Learning with Local and Global RepresentationsPreprint

Liang, P. P., Liu, T., Ziyin, L., Allen, N. B., Auerbach, R. P., Brent, D., Salakhutdinov, R., & Morency, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.01523 · Cited by 375 · see citing works

Adaptive Personalized Federated LearningPreprint

Deng, Y., Kamani, M. M., & Mahdavi, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13461 · Cited by 366 · see citing works

Multi-center federated learning: clients clustering for better personalization

Long, G., Xie, M. X., Shen, T., Zhou, T., Wang, X., & Jiang, J. B. (2022) · World Wide Web · doi:10.1007/s11280-022-01046-x · Cited by 359 · see citing works

Three 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

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Federated Learning for Open Banking

Long, G., Tan, Y., Jiang, J. B., & Chengqi Zhang, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63076-8_17 · Cited by 292 · see citing works

Deep learning for electronic health records: A comparative review of multiple deep neural architectures

Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247 · see citing works

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

Liu, B., Wang, L., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2931179 · Cited by 242 · see citing works

A <scp>uror</scp>: defending against poisoning attacks in collaborative deep learning systems

Shen, S., Tople, S., & Saxena, P. (2016) · Proceedings of the 32nd Annual Conference on Computer Security Applications · doi:10.1145/2991079.2991125 · Cited by 228 · see citing works

Casting out Demons: Sanitizing Training Data for Anomaly Sensors

Cretu, G. F., Stavrou, A., Locasto, M. E., Stolfo, S. J., & Keromytis, A. D. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.11 · Cited by 226 · see citing works

Learning to Detect Malicious Clients for Robust Federated LearningPreprint

Li, S., Cheng, Y. J., Wang, W., Liu, Y., & Chen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00211 · Cited by 202 · see citing works

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis

Lee, J., Sun, J., Wang, F., Wang, S., Jun, C.-H., & Jiang, X. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.7744 · Cited by 196 · see citing works

Stronger Data Poisoning Attacks Break Data Sanitization Defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2018) · arXiv · doi:10.48550/arxiv.1811.00741 · Cited by 189 · see citing works

Stronger data poisoning attacks break data sanitization defenses

Koh, P. W., Steinhardt, J., & Liang, P. (2021) · Machine Learning · doi:10.1007/s10994-021-06119-y · Cited by 189 · see citing works

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Lin, Y.-W., Zhou, Y., Faghri, F., Shaw, M. J., & Campbell, R. H. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218942 · Cited by 186 · see citing works

A Little Is Enough: Circumventing Defenses For Distributed LearningPreprint

Baruch, M., Baruch, G., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06156 · Cited by 184 · see citing works

Predicting sepsis with a recurrent neural network using the MIMIC III database

Scherpf, M., Gräßer, F., Malberg, H., & Zaunseder, S. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103395 · Cited by 167 · see citing works

Promoting innovation in healthcare

Kelly, C. J., & Young, A. J. (2017) · Future Healthcare Journal · doi:10.7861/futurehosp.4-2-121 · Cited by 148 · see citing works

Federated Tensor Factorization for Computational Phenotyping

Kim, Y., Sun, J., Yu, H., & Jiang, X. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098118 · Cited by 128 · see citing works

Abnormal client behavior detection in federated learning

Li, S., Cheng, Y., Liu, Y., Wang, W., & Chen, T. (2019) · arXiv · doi:10.48550/arxiv.1910.09933 · Cited by 122 · see citing works

Wearable Fall Detector using Integrated Sensors and Energy Devices

Jung, S., Hong, S., Kim, J., Lee, S., Hyeon, T., Lee, M., & Kim, D. (2015) · Scientific Reports · doi:10.1038/srep17081 · Cited by 99 · see citing works

A Framework for Evaluating Gradient Leakage Attacks in Federated LearningPreprint

Wei, W., Liu, L., Loper, M. L., Chow, K. H., Gürsoy, M. E., Truex, S., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10397 · Cited by 92 · see citing works

Open innovation as a new paradigm for global collaborations in health

Dandonoli, P. (2013) · Globalization and Health · doi:10.1186/1744-8603-9-41 · Cited by 69 · see citing works

Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study

Wu, R. C., Liaqat, D., de Lara, E., Son, T., Rudzicz, F., Alshaer, H., Abed-Esfahani, P., & Gershon, A. S. (2018) · JMIR mhealth and uhealth · doi:10.2196/10046 · Cited by 61 · see citing works

Towards sustainable healthcare system performance in the 21st century in high-income countries: a protocol for a systematic review of the grey literature

Braithwaite, J., Zurynski, Y. A., Ludlow, K., Holt, J., Augustsson, H., & Campbell, M. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025892 · Cited by 51 · see citing works

Open Innovation in Medical and Pharmaceutical Research: A Literature Landscape Analysis

Yeung, A. W. K., Atanasov, A. G., Sheridan, H., Klager, E., Eibensteiner, F., Völkl-Kernstock, S., Kletečka-Pulker, M., Willschke, H., & Schaden, E. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.587526 · Cited by 48 · see citing works

A General Framework for Diagnosis Prediction via Incorporating Medical Code Descriptions

Ma, F., Wang, Y., Xiao, H., Yuan, Y., Chitta, R., Zhou, J. L., & Gao, J. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621395 · Cited by 31 · see citing works

Variables influencing wearable sensor outcome estimates in individuals with stroke and incomplete spinal cord injury: a pilot investigation validating two research grade sensors

Jayaraman, C., Mummidisetty, C. K., Mannix-Slobig, A., Koch, L. L. M., & Jayaraman, A. (2018) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-018-0358-y · Cited by 27 · see citing works

FedProto: Federated Prototype Learning over Heterogeneous Devices.Preprint

Tan, Y., Long, G., Liu, L., Zhou, T., & Jiang, J. B. (2021) · arXiv (Cornell University) · Cited by 21 · see citing works

Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortality

Sharma, P., Shamout, F. E., & Clifton, D. A. (2019) · arXiv · doi:10.48550/arxiv.1912.00354 · Cited by 18 · see citing works

FedProto: Federated Prototype Learning across Heterogeneous Clients

Tan, Y., Long, G., Liu, L., Zhou, T., Lu, Q., Jiang, J. B., & Zhang, C. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20819 · Cited by 809 · see citing works

Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues

Rahman, 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

Multi-center federated learning: clients clustering for better personalization

Long, G., Xie, M. X., Shen, T., Zhou, T., Wang, X., & Jiang, J. B. (2022) · World Wide Web · doi:10.1007/s11280-022-01046-x · Cited by 359 · 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

Handling 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

Emerging trends in federated learning: from model fusion to federated X learning

Ji, S., Tan, Y., Saravirta, T., Yang, Z. Q., Liu, Y., Vasankari, L., Pan, S., Long, G., & Walid, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02119-1 · Cited by 171 · see citing works

Personalized Federated Learning With a Graph

Chen, F., Long, G., Wu, Z., Zhou, T., & Jiang, J. B. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/357 · Cited by 88 · see citing works

Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

Patel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 84 · see citing works

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 85 · see citing works

Dual Personalization on Federated Recommendation

Zhang, C., Long, G., Zhou, T., Yan, P., Zhang, Z., Zhang, C., & Yang, B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/507 · Cited by 77 · see citing works

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 66 · see citing works

Multimodal biometric authentication method by federated learning

Coelho, K. K., Tristão, E. T., Nogueira, M., Vieira, A. B., & Nacif, J. A. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105022 · Cited by 62 · see citing works

A survey on federated learning for security and privacy in healthcare applications

Coelho, K. K., Nogueira, M., Vieira, A. B., Silva, E. F., & Nacif, J. A. M. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.05.012 · Cited by 64 · see citing works

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 · see citing works

A Class-Imbalanced Heterogeneous Federated Learning Model for Detecting Icing on Wind Turbine Blades

Cheng, X., Shi, F., Liu, Y., Zhou, J., Liu, X., & Huang, L. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3167467 · Cited by 53 · see citing works

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

Babar, M. A., Qureshi, B., & Koubâa, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0302539 · Cited by 53 · see citing works

Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis

Hernandez-Cruz, N., Saha, P., Sarker, M. M. K., & Noble, J. A. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8090099 · Cited by 48 · see citing works

Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

Chen, S., Long, G., Shen, T., & Jiang, J. B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/393 · Cited by 52 · see citing works

Sequential Diagnosis Prediction with Transformer and Ontological Representation

Peng, X., Long, G., Shen, T., Wang, S., & Jiang, J. B. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00060 · Cited by 39 · see citing works

A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data

Hindistan, Y. S., & Yetkin, E. F. (2023) · IEEE Access · doi:10.1109/access.2023.3235969 · Cited by 36 · see citing works

Effectiveness of Decentralized Federated Learning Algorithms in Healthcare: A Case Study on Cancer Classification

Subramanian, M., Rajasekar, V., Easwaramoorthy, S. V., Shanmugavadivel, K., & Nandhini, P. S. (2022) · Electronics · doi:10.3390/electronics11244117 · Cited by 34 · see citing works

Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications

Alsamhi, S. H., Hawbani, A., Shvetsov, A. V., & Kumar, S. (2023) · Advances in Human-Computer Interaction · doi:10.1155/2023/9992393 · Cited by 25 · see citing works

AI based energy harvesting security methods: A survey

Mohammadi, M., & Sohn, I. (2023) · ICT Express · doi:10.1016/j.icte.2023.06.002 · Cited by 22 · see citing works

Cyber threats in mobile healthcare applications: systematic review of enabling technologies, threat models, detection approaches, and future directions

Ikegwu, A. C., Alo, U. R., & Nweke, H. F. (2025) · Discover Computing · doi:10.1007/s10791-025-09686-z · Cited by 24 · see citing works

FedProto: Federated Prototype Learning over Heterogeneous Devices.Preprint

Tan, Y., Long, G., Liu, L., Zhou, T., & Jiang, J. B. (2021) · arXiv (Cornell University) · Cited by 21 · see citing works

FedAGA: A federated learning framework for enhanced inter-client relationship learning

Ge, J., Xu, G., Lu, J., Xu, C., Sheng, Q. Z., & Zheng, X. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111399 · Cited by 16 · see citing works

Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack

Takahashi, H., Liu, J., & Liu, Y. (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/cvpr52729.2023.01174 · Cited by 15 · see citing works

Privacy Issues, Attacks, Countermeasures and Open Problems in Federated Learning: A Survey

Guembe, B., Misra, S., & Azeta, A. A. (2024) · Applied Artificial Intelligence · doi:10.1080/08839514.2024.2410504 · Cited by 18 · see citing works

Federated Fingerprint Learning with Heterogeneous Architectures

Che, T., Zhang, Z., Zhou, Y., Zhao, X., Liu, J., Jiang, Z., Yan, D., Jin, R., & Dou, D. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00013 · Cited by 12 · see citing works

Social Welfare Maximization in Cross-Silo Federated Learning

Chen, J., Hu, Q., & Jiang, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746813 · Cited by 12 · see citing works

Healthcare Innovation in Greece: The Views of Private Health Entrepreneurs on Implementing Innovative Plans

Apostolopoulos, S., Makris, I., & Stavroyiannis, S. (2022) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc8020078 · Cited by 11 · see citing works

Federated Learning and Adaptive Privacy Preserving in Healthcare

Madhavi, K. R., Suri, V. K., Mahalakshmi, V. J., Reddy, R. O., & Reddy, C. S. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27499-2_51 · Cited by 9 · see citing works

Towards driver distraction detection: a privacy-preserving federated learning approach

Zhou, W., Jia, Z., Feng, C., Lu, H., Lyu, F., & Li, L. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01639-5 · Cited by 9 · see citing works

Next-Generation Machine Learning in Healthcare Fraud Detection: Current Trends, Challenges, and Future Research Directions

Razzaq, K., & Shah, M. H. (2025) · Information · doi:10.3390/info16090730 · Cited by 12 · see citing works

Balancing privacy and performance in healthcare: A federated learning framework for sensitive data

Tanveer, F., Iradat, F., Iqbal, W., AlSagri, H. S., Alhakbani, H. A., Ahmad, A., & Khan, F. A. (2025) · Digital Health · doi:10.1177/20552076251381769 · Cited by 9 · see citing works

Assessment of the Artificial Intelligence and Quantum Computing in the Smart Management Information Systems

Efe, A. (2023) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.1190670 · Cited by 9 · see citing works

Investigating privacy-preserving machine learning for healthcare data sharing through federated learning

Ahamed, S. K., Nishant, N., Selvaraj, A., Gandhewar, N., Srithar, A., & Baseer, K. K. (2023) · THE SCIENTIFIC TEMPER · doi:10.58414/scientifictemper.2023.14.4.37 · Cited by 7 · see citing works

Predicting Outcomes for Cancer Patients with Transformer-Based Multi-task Learning

Gerrard, L., Peng, X., Clarke, A., Schlegel, C., & Jiang, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_31 · Cited by 7 · see citing works

Federated Learning Approach for Measuring the Response of Brain Tumors to Chemotherapy

Atef, O., Salam, M. A., & Abdelsalam, H. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131060 · Cited by 6 · see citing works

A Scalable Asynchronous Federated Learning for Privacy-Preserving Real-Time Surveillance Systems

Hagos, D. H., Tankard, E., & Rawat, D. B. (2023) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps57453.2023.10226108 · Cited by 6 · see citing works

Privacy-Preserving Medical AI on the Edge: A Federated Learning and Blockchain Approach for 6G Healthcare Networks

Sajiv, G., & Meenakshisundaram, N. (2025) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc65114.2025.11212375 · Cited by 5 · see citing works

Personalized Federated Learning with Robust Clustering Against Model Poisoning

Ma, J., Xie, M. X., & Long, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22137-8_18 · Cited by 5 · see citing works

A systematic review on privacy preservation in federated learning

Sum, A. S. I., Pritee, Z. T., Saha, A. K., Mahmud, M. A., & Nur, K. M. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01229-x · Cited by 6 · see citing works

Multicriteria client selection model using class topper optimization based optimal federated learning for healthcare informatics

Narwaria, M., & Jaiswal, S. (2024) · Cluster Computing · doi:10.1007/s10586-024-04466-8 · Cited by 4 · see citing works

Digital Psychotherapy: Technological Acceptance Model on Multi Linear Regression Analysis

Panduwiyasa, H., & Wibowo, F. S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.167 · Cited by 4 · see citing works

A privacy-preserving federated learning architecture implementing data ownership and portability on edge end-points

Mpofu, P., Kembo, S. H., Chimbwanda, M., Jacques, S., Chitiyo, N., & Zvarevashe, K. (2023) · International Journal of Industrial Engineering and Operations Management · doi:10.1108/ijieom-02-2023-0020 · Cited by 4 · see citing works

Privacy protection of quantum BP neural network based on game theory

Lin, Y., Chang, Y. Z., Huang, S., & Zhang, S. (2023) · Physica Scripta · doi:10.1088/1402-4896/acf73d · Cited by 4 · see citing works

A Survey on Privacy-Preserving Enabled Healthcare Services Focusing on Robust Aggregation

Aggarwal, M., Khullar, V., Goyal, N., Jayaprakash, B., Bharathi, B., Sahu, P. K., Sharma, G., & Bhardwaj, V. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-01020-1 · Cited by 4 · see citing works

Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems

Lamem, M. F. H., & Sahid, M. I. (2025) · Digital Health · doi:10.1177/20552076251406306 · Cited by 4 · see citing works

Privacy Prevention Techniques in Cyber Physical System for Pharmaceutical Care Services

Devliyal, S., Sharma, S., & Goyal, H. R. (2023) · International Conference on Power, Instrumentation, Control and Computing (PICC) · doi:10.1109/picc57976.2023.10142327 · Cited by 3 · see citing works

Anomaly Detection in Medical IoT Devices Using Federated Learning

Namratha, M., Anusree Manoj, K., Niha, Srinivasan, P., & Arpana, M. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-0769-4_25 · Cited by 3 · see citing works

Can Fairness and Robustness Be Simultaneously Achieved Under Byzantine Attacks?

Zheng, H., Wang, R., Xiao, W., & Ling, Q. H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890227 · Cited by 3 · see citing works

Survey: An Overview on Privacy Preserving Federated Learning in Health Data

Elahi, M., Cui, H., & Kaosar, M. G. (2023) · Computer Networks and Communications · doi:10.37256/cnc.1120231992 · Cited by 3 · see citing works

Alliance Makes Difference? Maximizing Social Welfare in Cross-Silo Federated Learning

Chen, J., Hu, Q., & Jiang, H. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3320550 · Cited by 3 · see citing works

Robust Clustered Federated Learning with Bootstrap Median-of-Means

Xie, M. X., Ma, J., Long, G., & Zhang, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25158-0_19 · Cited by 2 · see citing works

Adaptive asynchronous federated learning for heterogeneous clients

Liang, Y., Ouyang, C., & Chen, X. (2022) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis58238.2022.00090 · Cited by 2 · see citing works

A Progressive Sampling Method for Dual-Node Imbalanced Learning with Restricted Data Access

Qiu, Y., Chen, W., & Xu, M. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00060 · Cited by 2 · see citing works

An Efficient Federated Convolutional Neural Network Scheme with Differential Privacy

Zhang, D., Chen, X., & Shi, J. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23098-1_11 · Cited by 1 · see citing works

Privacy Pinnacle: Improvising Healthcare Data Security Through Federated Learning and Blockchain Framework

Amreen, G., & Kanavalli, A. (2024) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt61001.2024.10724371 · Cited by 1 · see citing works

Privacy Considerations in AI and Digital Health Systems

Kavitha, C. R., & Sowmya, K. N. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-9591-2.ch004 · Cited by 2 · see citing works

Unwinding the Significance of Federated Learning for Smart Agriculture

Bhat, A. N., Sahil, & Kumar, G. (2024) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/globalaisummit62156.2024.10947983 · Cited by 1 · see citing works

Explainable Federated Stacking Models with Encrypted Gradients for Secure Kidney Medical Imaging Diagnosis

Tanim, S. A., Aurnob, A. R., Islam, M. R., Miah, M. S. U., Rahman, M. M., & Mahmud, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-6576-1_8 · Cited by 1 · see citing works

AI-Driven Framework for Enhancing Digital Well-Being through Smartphone Usage Monitoring Among University Students

B.Renugadevi, Sanjay, D., Selvaganesh, M., Vitchu, R., Kavirethinam, C., & Sujithran, G. (2025) · International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) · doi:10.1109/iemecon69302.2025.11365909 · Cited by 1 · see citing works

Federated Latent Factorization of Tensors for Privacy-Preserving Representation Learning to Large-Scale Dynamic Weighted Directed Network

Wu, D., Zhong, S., He, Y., Luo, X., & Gao, X. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3683950 · Cited by 2 · see citing works

WeightRelay: Efficient Heterogeneous Federated Learning on Time Series

Tang, W., & Long, G. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8388-9_11 · Cited by 1 · see citing works

FedMqADV: A Unified Framework for End-to-End Evaluation of MQTT-Based Federated Learning in Adversarial Setting

Ndiaye, N. G., Ruland, C., Waedt, K., Zeddini, O., & Kirdan, E. (2025) · IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) · doi:10.1109/icps65515.2025.11087915 · Cited by 1 · see citing works

Collaborative Artifical Intelligence Concept: Federated Learning Review

Nergiz, M. E. (2022) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1130789 · Cited by 1 · see citing works

Machine Teaching-Based Efficient Labelling for Cross-unit Healthcare Data Modelling

Wang, Y., Peng, X., Clarke, A., Schlegel, C., & Jiang, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_26

Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism

Lakshmi, K. P., & Swapnika, K. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140541

WITHDRAWN: Performance Analysis of Personalized Federated Learning Algorithms for Image ClassificationPreprint

Hosain, M. T., Abir, M. R., Jim, J. R., Zaman, A., & Mridha, M. F. (2023) · Research Square · doi:10.21203/rs.3.rs-3056513/v1

Sustainable Development for Smart Healthcare using Privacy-preserving Blockchain-based FL Framework

Renuka, D. K., Anusuya, R., & Kumar, L. A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815196290124010017

Explaining Federated Learning Through Concepts in Image Classification

Shen, J., Tao, X., Li, L., Li, Z., & Wang, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0808-6_19

Integration of federated learning paradigms into electronic health record systems

Obakhena, H. I., Imoize, A. L., & Anyasi, F. I. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00017-5

An intelligent optimized secure blockchain mechanism for cloud auditing

Kumar, R., & Bhatia, M. P. S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124593

Claimsformer: Pretrained Transformer for Administrative Claims Data to Predict Chronic Conditions

Gerrard, L., Peng, X., Clarke, A., & Long, G. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0351-0_26 · Cited by 1 · see citing works

Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning

Li, S., & Zhang, B. (2025) · ITM Web of Conferences · doi:10.1051/itmconf/20257003029

Split Heterogeneous Federated Convolutional Neural Network for Blade Icing Detection

Cheng, X., Shi, F., Liu, X., & Chen, S. (2025) · Engineering Applications of Computational Methods · doi:10.1007/978-981-96-6763-5_8

Artificial Intelligence-Enabled Federated Learning Techniques in Healthcare Sector

Sharma, S., & Gour, R. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-80949-1_4

Federated Learning in Healthcare Technology: Challenges, Solutions and Opportunities

Abha, E. E. M. C., Sajin, M. I. A., Suva, M. W., Akter, S., Abir, M. R., Reaj, M. A. H., & Rafiq, J. I. (2025) · Studies in computational intelligence · doi:10.1007/978-981-96-8353-6_1

Federated Learning and Differential Privacy Preserve for Precise Disease Prediction

Sundar, K., Ravikumar, S., Babu, R., Eugene Berna, I., & K, V. (2025) · International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM) · doi:10.1109/iipem65914.2025.11548043

FedMedNet: A Federated Privacy-Preserving Framework for Multi-Modal EHR-Based Clinical Outcome Prediction

Nair, R. R., Babu, T. L., & Nayak, D. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.05.102

Privacy-Preserving Collaborative Learning for Chronic Heart Failure: A Federated Approach with Explainable Deep Learning and Real-Time Treatment Adaptation

Tabassum, S., & Senthilkumar, P. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-25694-2_42

Empowering Women through Self-determined Tracking and Self-initiated Sharing of Health Data with Medical Professionals

Grimme, S. A., Spoerl, S. M., Ganjeh, P. S., Meyer, J., Boll, S., & Koelle, M. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3837078

Resilience of Federated Learning for Credit Risk Forecasting Under Heterogeneity, Label Noise and Partial Participation

Enang, I. J., Leevy, J. L., Billion-Polak, P., & Khoshgoftaar, T. M. (2026) · IEEE Access · doi:10.1109/access.2026.3743719

Similarity-based Soft Supervision and Classifier Adjustment for Federated Long-Tailed Learning

Wang, P., Wang, L., Li, H., & Zhou, J. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729886

Federated Learning for Private Health Data Training

Vilas, N. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i02.41434

Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems

Shafik, W. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_37

Federated learning for privacy-preserving AI in healthcare applications

Nagvekar, M., Borkar, P. S., Badhiye, S. S., Janardhan, D. M., Sa, J., & Yenkikar, A. (2026) · AIP conference proceedings · doi:10.1063/5.0326181

Privacy-preserving federated learning for multi-institutional healthcare systems

Akavaram, S. (2025) · World Journal of Advanced Research and Reviews · doi:10.5281/zenodo.17345884

Privacy-Aware Federated Learning Architectures for Cross-Institutional Healthcare Collaboration

Aarthi, S., & Gohil, J. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7426-0.ch006