Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141

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

The multimodal brain tumor image segmentation benchmark (BRATS)

Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781

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

The 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 4,015

Federated Learning: Strategies for Improving Communication Efficiency

Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766

Industry 4.0: state of the art and future trends

Xu, L. D., Xu, E. L., & Li, L. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1444806 · Cited by 3,405

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076

A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications

Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793

The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism

Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780

Federated Learning With Differential Privacy: Algorithms and Performance Analysis

Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400

Federated Learning with Non-IID Data

Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 2,055

The eICU Collaborative Research Database, a freely available multi-center database for critical care research

Pollard, T. J., Johnson, A. E. W., Raffa, J. D., Celi, L. A., Mark, R. G., & Badawi, O. (2018) · Scientific Data · doi:10.1038/sdata.2018.178 · Cited by 1,993

Untitled work

(2013) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 1,959

A survey on federated learning

Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106775 · Cited by 1,899

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Hasan, M., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,722

Challenges in Representation Learning: A Report on Three Machine Learning Contests

Goodfellow, I. J., Erhan, D., Carrier, P. L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.-H., et al. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-42051-1_16 · Cited by 1,719

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Lee, L., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., & Hui, P. (2024) · Foundations and Trends® in Human–Computer Interaction · doi:10.1561/1100000095 · Cited by 1,686

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

Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016) · arXiv · doi:10.48550/arxiv.1610.02527 · Cited by 1,563

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

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

Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection

Schmidt, P., Reiss, A., Dürichen, R., Marberger, C., & Laerhoven, K. V. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3242985 · Cited by 1,508

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

Towards Personalized Federated Learning

Tan, A. Z., Yu, H., Cui, L., & Yang, Q. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3160699 · Cited by 1,474

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., & He, B. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3124599 · Cited by 1,407

2014 IEEE Global Communications Conference

Masip Bruin, X., Ramirez, W., Siddiqui Shoaib, M., Serral Gracià, R., Montero Banegas, D. T., & Yannuzzi (2014) · doi:10.1109/globecom18177.2014 · Cited by 1,405

A Survey on Homomorphic Encryption Schemes: Theory and Implementation

Acar, A., Aksu, H., Uluagac, A. S., & Conti, M. (2019) · ACM Computing Surveys · doi:10.1145/3214303 · Cited by 1,397

Federated Multi-Task LearningPreprint

Smith, V., Chiang, C.-K., Sanjabi, M., & Talwalkar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10467 · Cited by 1,282

Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT

Lu, Y., Huang, X., Dai, Y., Maharjan, S., & Zhang, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2942190 · Cited by 1,245

All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda

Lee, L.-H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., & Hui, P. (2021) · arXiv · doi:10.48550/arxiv.2110.05352 · Cited by 1,088

Federated Learning of Deep Networks using Model AveragingPreprint

McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005

FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare

Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020) · IEEE Intelligent Systems · doi:10.1109/mis.2020.2988604 · Cited by 1,005

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

Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961

Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0

Swan, M. (2012) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan1030217 · Cited by 934

LEAF: A Benchmark for Federated Settings

Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1812.01097 · Cited by 894

Verifynet: Secure and verifiable federated learning

Xu, G., Li, H., Liu, S., Yang, K., & Lin, X. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2929409 · Cited by 839

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing

(1985) · doi:10.1109/icassp.1985 · Cited by 820

Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

de Alwis, C., Kalla, A., Pham, Q., Kumar, P., Dev, K., Hwang, W., & Liyanage, M. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3071496 · Cited by 809

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

Abdulrahman, 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

Security of the Internet of Things: Vulnerabilities, Attacks, and Countermeasures

Bütün, İ., Osterberg, P., & Song, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2953364 · Cited by 792

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images

Uçar, F., & Korkmaz, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109761 · Cited by 768

Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020) · IEEE Access · doi:10.1109/access.2020.3013541 · Cited by 733

COVID-19 Image Data CollectionPreprint

Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722

The State of Elliptic Curve Cryptography

Koblitz, N., Menezes, A., & Vanstone, S. A. (2000) · Designs Codes and Cryptography · doi:10.1023/a:1008354106356 · Cited by 687

FedML: A Research Library and Benchmark for Federated Machine LearningPreprint

He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13518 · Cited by 668

Internet of things (IoT) applications to fight against COVID-19 pandemic

Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.041 · Cited by 664

2020 IEEE International Conference on Big Data (Big Data)

(2020) · doi:10.1109/bigdata50022.2020 · Cited by 663

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663

Scheduling Policies for Federated Learning in Wireless Networks

Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640

A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

Yin, X., Zhu, Y., & Hu, J. (2022) · ACM Computing Surveys · doi:10.1145/3460427 · Cited by 638

Untitled work

Yang (2019) · doi:10.1145/3339474 · Cited by 632

Clock Synchronization of Wireless Sensor Networks

Wu, Y., Chaudhari, Q. M., & Serpedin, E. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.938757 · Cited by 625

Health Behavior, Illness Behavior and Sick Role behavior

Kasl, S. V., & Cobb, S. (1966) · Archives of Environmental Health An International Journal · doi:10.1080/00039896.1966.10664365 · Cited by 622

Botnets and Internet of Things Security

Bertino, E., & Islam, N. (2017) · Computer · doi:10.1109/mc.2017.62 · Cited by 618

Reliable Federated Learning for Mobile Networks

Kang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020) · IEEE Wireless Communications · doi:10.1109/mwc.001.1900119 · Cited by 617

Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices

Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., & Liu, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3017377 · Cited by 613

Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

Chen, Y., Sun, X., & Jin, Y. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2953131 · Cited by 597

Federated 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

Artificial intelligence for the detection of COVID-19 pneumonia on chest ct using multinational datasets

Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587

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

Federated Optimization:Distributed Optimization Beyond the DatacenterPreprint

Konečný, J., McMahan, B., & Ramage, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03575 · Cited by 547

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

Hidasi, B., Quadrana, M., Karatzoglou, A., & Tikk, D. (2016) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2959100.2959167 · Cited by 542

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Loey, M., Smarandache, F., & M. Khalifa, N. E. (2020) · Symmetry · doi:10.3390/sym12040651 · Cited by 538

Remote patient monitoring: a comprehensive study

Malasinghe, L. P., Ramzan, N., & Dahal, K. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0598-x · Cited by 534

2015 IEEE International Conference on Data Mining

(2015) · doi:10.1109/icdm36327.2015 · Cited by 534

IoT in smart cities: A survey of technologies, practices and challenges

Syed, A. S., Sierra-Sosa, D., Kumar, A., & Elmaghraby, A. (2021) · Smart Cities · doi:10.3390/smartcities4020024 · Cited by 527

Split learning for health: Distributed deep learning without sharing raw patient data

Vepakomma, P., Gupta, O., Swedish, T., & Raskar, R. (2018) · arXiv · doi:10.48550/arxiv.1812.00564 · Cited by 513

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Khan, L. U., Pandey, S. R., Tran, N. H., Saad, W., Han, Z., Nguyen, M. N. H., & Hong, C. S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900649 · Cited by 506

HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous ClientsPreprint

Diao, E., Ding, J., & Tarokh, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01264 · Cited by 499

Iot-based smart cities: A survey

Arasteh, H., Hosseinnezhad, V., Loia, V., Tommasetti, A., Troisi, O., Shafie‐khah, M., & Siano, P. (2016) · IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) · doi:10.1109/eeeic.2016.7555867 · Cited by 493

An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare

Yang, Z., Zhou, Q., Lei, L., Zheng, K., & Xiang, W. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0644-9 · Cited by 490

Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results

Li, X., Gu, Y., Dvornek, N., Staib, L. H., Ventola, P., & Duncan, J. S. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101765 · Cited by 480

Recent Advances in the Internet-of-Medical-Things (IoMT) Systems Security

Ghubaish, A., Salman, T., Zolanvari, M., Ünal, D., Al-Ali, A. K., & Jain, R. K. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3045653 · Cited by 480

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

Luo, S., Chen, X., Wu, Q., Zhou, Z., & Yu, S. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3003744 · Cited by 472

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

Supatto, W. (2020) · doi:10.1109/isbi45749.2020 · Cited by 472

Practical Secure Aggregation for Federated Learning on User-Held DataPreprint

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04482 · Cited by 468

Federated learning for 6G communications: Challenges, methods, and future directions

Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications · doi:10.23919/jcc.2020.09.009 · Cited by 464

Federated Learning for intrusion detection system: Concepts, challenges and future directions

Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457

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 442

Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103291 · Cited by 437

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar, H., Wang, Y., Orji, R., & Huang, S. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3001216 · Cited by 416

Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413

Federated learning for the Internet of Things: Applications, challenges, and opportunities

Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, A. S. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.004.2100182 · Cited by 412

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

(2020) · doi:10.1109/globecom42002.2020 · Cited by 410

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

AI in Health: State of the Art, Challenges, and Future Directions

Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398

Internet of Medical Things (IoMT): Overview, Emerging Technologies, and Case Studies

Razdan, S., & Sharma, S. (2021) · IETE Technical Review · doi:10.1080/02564602.2021.1927863 · Cited by 398

FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

Wu, Q., Chen, X., Zhou, Z., & Zhang, J. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3045266 · Cited by 392

ICC 2021 - IEEE International Conference on Communications

(2021) · doi:10.1109/icc42927.2021 · Cited by 392

A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology

Singh, S., Rathore, S., Alfarraj, O., Tolba, A. K. R., & Yoon, B. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.11.028 · Cited by 391

Masking against Side-Channel Attacks: A Formal Security Proof

Prouff, E., & Rivain, M. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38348-9_9 · Cited by 391

Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study

Hady, A. A., Ghubaish, A., Salman, T., Ünal, D., & Jain, R. K. (2020) · IEEE Access · doi:10.1109/access.2020.3000421 · Cited by 390

Accuracy of wrist-worn heart rate monitors

Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3340 · Cited by 386

Survey of Personalization Techniques for Federated Learning

Kulkarni, V., Kulkarni, M., & Pant, A. (2020) · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) · doi:10.1109/worlds450073.2020.9210355 · Cited by 381

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

Proceedings of the 10th ACM Conference on Recommender Systems

(2016) · doi:10.1145/2959100 · Cited by 371

Using X-ray images and deep learning for automated detection of coronavirus disease

Asnaoui, K. E., & Chawki, Y. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1767212 · Cited by 369

Block-DEF: A secure digital evidence framework using blockchain

Tian, Z., Li, M., Qiu, M., Sun, Y., & Su, S. (2019) · Information Sciences · doi:10.1016/j.ins.2019.04.011 · Cited by 366

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Zeadally, S., Adi, E., Baig, Z. A., & Khan, I. A. (2020) · IEEE Access · doi:10.1109/access.2020.2968045 · Cited by 359

Personalized Federated Learning With Differential Privacy

Hu, R., Guo, Y., Li, H., Pei, Q., & Gong, Y. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2991416 · Cited by 350

Federated 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 332

Dynamic-Fusion-Based Federated Learning for COVID-19 Detection

Zhang, W., Zhou, T., Lu, Q., Wang, X., Zhu, C., Sun, H., Wang, Z., Lo, S. K., & Wang, F.-Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056185 · Cited by 329

Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions

Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 326

Fusion of Federated Learning and Industrial Internet of Things: A survey

Boobalan, P., Ramu, S. P., Pham, Q., Dev, K., Pandya, S., Maddikunta, P. K. R., Gadekallu, T. R., & Huynh‐The, T. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.109048 · Cited by 321

Introduction to Side-Channel Attacks

Standaert, F. (2009) · Integrated circuits and systems · doi:10.1007/978-0-387-71829-3_2 · Cited by 319

On Safeguarding Privacy and Security in the Framework of Federated Learning

Ma, C., Li, J., Ding, M., Yang, H. H., Shu, F., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Network · doi:10.1109/mnet.001.1900506 · Cited by 318

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

So, J., Guler, B., & Avestimehr, A. S. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3054610 · Cited by 318

Multi-Armed Bandit-Based Client Scheduling for Federated Learning

Xia, W., Quek, T. Q. S., Guo, K., Wen, W., Yang, H. H., & Zhu, H. (2020) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3008091 · Cited by 316

The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

Vavoulas, G., Chatzaki, C., Malliotakis, T., Pediaditis, M., & Tsiknakis, M. (2016) · ICT4AgeingWell · doi:10.5220/0005792401430151 · Cited by 316

Medical Robots: Current Systems and Research Directions

Beasley, R. A. (2012) · Journal of Robotics · doi:10.1155/2012/401613 · Cited by 312

A survey of federated learning for edge computing: Research problems and solutions

Xia, Q., Ye, W., Tao, Z., Wu, J., & Li, Q. (2021) · High-Confidence Computing · doi:10.1016/j.hcc.2021.100008 · Cited by 310

Internet of Medical Things (IoMT) - An overview

Vishnu, S., Ramson, S. R. J., & Jegan, R. (2020) · International Conference on Devices, Circuits and Systems (ICDCS) · doi:10.1109/icdcs48716.2020.243558 · Cited by 310

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Spreitzer, R., Moonsamy, V., Korak, T., & Mangard, S. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2779824 · Cited by 308

Securing internet of medical things systems: limitations, issues and recommendations

Yaacoub, J.-P. A., Noura, M., Noura, H. N., Salman, O., Yaacoub, E., Couturier, R., & Chehab, A. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.12.028 · Cited by 308

A hybrid solution for privacy preserving medical data sharing in the cloud environment

Yang, J., Li, J., & Niu, Y. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.06.004 · Cited by 299

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 298

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

Asiri, N., Hussain, M., Adel, F. A., & Alzaidi, N. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.009 · Cited by 292

2015 IEEE Security and Privacy Workshops

(2015) · doi:10.1109/spw33959.2015 · Cited by 292

25th ACM/IEEE Design Automation Conference ®

(1986) · Computer · doi:10.1109/mc.1986.1663069 · Cited by 288

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

Nguyen, D. C., Ding, M., Pathirana, P. N., & Seneviratne, A. (2021) · IEEE Access · doi:10.1109/access.2021.3093633 · Cited by 286

Untitled work

Nguyen (2022) · doi:10.1145/3453476 · Cited by 286

A secure healthcare 5.0 system based on blockchain technology entangled with federated learning technique

Rehman, 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

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan

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

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

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Dou, Q., So, T. Y., Jiang, M., Liu, Q., Vardhanabhuti, V., Kaissis, G., Li, Z., Si, W., Lee, H. H. C., Yu, K., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00431-6 · Cited by 278

Emotion-aware connected healthcare big data towards 5G

Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273

Federated 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

A Federated Learning Based Privacy-Preserving Smart Healthcare System

Li, J., Meng, Y., Ma, L., Du, S., Zhu, H., Pei, Q., & Shen, X. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3098010 · Cited by 265

Homomorphic Encryption and Applications

Yi, X., Paulet, R., & Bertino, E. (2014) · SpringerBriefs in computer science · doi:10.1007/978-3-319-12229-8 · Cited by 256

A deep learning-based social distance monitoring framework for COVID-19

Ahmed, I., Ahmad, M., Rodrigues, J. J. P. C., Jeon, G., & Din, S. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102571 · Cited by 255

OpenFL: the open federated learning library

Foley, P., Sheller, M., Edwards, B., Pati, S., Riviera, W., Sharma, M., Moorthy, P. N., Wang, S.-H., Martin, J., Mirhaji, P., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac97d9 · Cited by 253

Resilient practical cooperative output regulation for MASs with unknown switching exosystem dynamics under DoS attacks

Deng, C., Zhang, D., & Feng, G. (2022) · Automatica · doi:10.1016/j.automatica.2022.110172 · Cited by 250

PySyft: A Library for Easy Federated Learning

Ziller, A., Trask, A., Lopardo, A., Szymkow, B., Wagner, B., Bluemke, E., Nounahon, J.-M., Passerat-Palmbach, J., Prakash, K., Rose, N., et al. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-70604-3_5 · Cited by 246

A Survey on Security Threats and Countermeasures in Internet of Medical Things (IoMT)

Papaioannou, M., Karageorgou, M., Mantas, G., Sucasas, V., Essop, I., Rodriguez, J., & Lymberopoulos, D. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4049 · Cited by 245

Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Li, J., Shao, Y., Wei, K., Ding, M., Ma, C., Shi, L. X., Han, Z., & Poor, H. V. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3138848 · Cited by 244

FedGraphNN: A Federated Learning System and Benchmark for Graph Neural NetworksPreprint

He, C., Balasubramanian, K., Ceyani, E., Yang, C., Xie, H., Sun, L., He, L., Yang, L., Yu, P. S., Rong, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07145 · Cited by 243

Federated learning improves site performance in multicenter deep learning without data sharing

Sarma, K. V., Harmon, S., Sanford, T., Roth, H. R., Xu, Z., Tetreault, J., Xu, D., Flores, M. G., Raman, A. G., Kulkarni, R., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa341 · Cited by 242

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

(2019) · doi:10.1109/icdcs47608.2019 · Cited by 239

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237

2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks

(2014) · doi:10.1109/dsn30607.2014 · Cited by 225

A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things

Hasan, M. K., Ghazal, T. M., Saeed, R. A., Pandey, B., Gohel, H., Eshmawi, A. A., Abdel‐Khalek, S., & Alkhassawneh, H. M. (2021) · IET Communications · doi:10.1049/cmu2.12301 · Cited by 225

Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data

Silva, S., Gutman, B. A., Romero, E., Thompson, P. M., Altmann, A., & Lorenzi, M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759317 · Cited by 225

MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients

Shorfuzzaman, M., & Hossain, M. S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107700 · Cited by 217

LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data

Li, H., Yin, Y., Fu, Z., Zhang, S., Deng, H., & Liu, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230706 · Cited by 212

Wearable medical devices for tele-home healthcare

Hung, K., Zhang, Y. T., & Tai, B. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2004.1404503 · Cited by 211

A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications

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

Motivating Workers in Federated Learning: A Stackelberg Game Perspective

Sarikaya, Y., & Erçetin, Ö. (2019) · IEEE Networking Letters · doi:10.1109/lnet.2019.2947144 · Cited by 209

Security in IoMT Communications: A Survey

Koutras, D., Stergiopoulos, G., Dasaklis, T. K., Kotzanikolaou, P., Glynos, D., & Douligeris, C. (2020) · Sensors · doi:10.3390/s20174828 · Cited by 205

Federated Learning for Breast Density Classification: A Real-World Implementation

Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60548-3_18 · Cited by 202

A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective

Lo, S. K., Lu, Q., Wang, C., Paik, H.-Y., & Zhu, L. (2022) · ACM Computing Surveys · doi:10.1145/3450288 · Cited by 202

Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

Guo, P., Wang, P., Zhou, J., Jiang, S., & Patel, V. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00245 · Cited by 193

Federated learning for privacy-preserving AI

Cheng, Y., Liu, Y., Chen, T., & Yang, Q. (2020) · Communications of the ACM · doi:10.1145/3387107 · Cited by 187

Differential privacy-enabled federated learning for sensitive health data

Choudhury, O., Gkoulalas-Divanis, A., Salonidis, T., Sylla, I., Park, Y., Hsu, G., & Das, A. (2019) · arXiv · doi:10.48550/arxiv.1910.02578 · Cited by 184

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Nguyen, D. C., Pathirana, P. N., Ding, M., & Seneviratne, A. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3058953 · Cited by 184

IoMT-SAF: Internet of Medical Things Security Assessment Framework

Alsubaei, F. S., Abuhussein, A., Shandilya, V., & Shiva, S. G. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100123 · Cited by 179

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

Bibi, N., Sikandar, M., Din, I. U., Almogren, A., & Ali, S. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/6648574 · Cited by 177

Analysis of Using Blockchain to Protect the Privacy of Drone Big Data

Lv, Z., Qiao, L., Hossain, M. S., & Choi, B. J. (2021) · IEEE Network · doi:10.1109/mnet.011.2000154 · Cited by 177

An Internet-of-Medical-Things-Enabled Edge Computing Framework for Tackling COVID-19

Rahman, M. A., & Hossain, M. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3051080 · Cited by 176

NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium

(2020) · doi:10.1109/noms47738.2020 · Cited by 172

Review of Security and Privacy for the Internet of Medical Things (IoMT)

Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., & Tsatsoulis, C. (2019) · 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) · doi:10.1109/dcoss.2019.00091 · Cited by 172

National Health Information Privacy: Regulations Under the Health Insurance Portability and Accountability Act

Gostin, L. O. (2001) · JAMA · doi:10.1001/jama.285.23.3015 · Cited by 165

Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

Kholod, I. I., Yanaki, E., Fomichev, D., Shalugin, E. D., Novikova, E. S., Filippov, E., & Nordlund, M. (2020) · Sensors · doi:10.3390/s21010167 · Cited by 165

Proceedings of the 20th ACM International Conference on Multimodal Interaction

(2018) · doi:10.1145/3242969 · Cited by 164

Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records

Che, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y. (2017) · 2017 IEEE International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2017.93 · Cited by 162

OCEANS 2018 MTS/IEEE Charleston

(2018) · doi:10.1109/oceans38223.2018 · Cited by 161

Federated Learning for Internet of Things

Zhang, T., He, C., Ma, T., Gao, L., Ma, M., & Avestimehr, S. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3485730.3493444 · Cited by 157

Enhancing Medical Smartphone Networks via Blockchain-Based Trust Management Against Insider Attacks

Meng, W., Li, W., & Zhu, L. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2921736 · Cited by 154

Homomorphic Encryption

Yi, X., Paulet, R., & Bertino, E. (2014) · SpringerBriefs in computer science · doi:10.1007/978-3-319-12229-8_2 · Cited by 152

EEG-Based Pathology Detection for Home Health Monitoring

Muhammad, G., Hossain, M. S., & Kumar, N. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3020654 · Cited by 148

Smart Home System Based on IOT Technologies

Jie, Y., Pei, J. Y., Li, J., Guo, Y. B., & Xu, W. X. (2013) · International Conference on Computational and Information Sciences · doi:10.1109/iccis.2013.468 · Cited by 148

IEEE INFOCOM 2018 — IEEE conference on computer communications workshops (INFOCOM WKSHPS)

(2018) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2018.8406775 · Cited by 144

The 2014 International Symposium on Networks, Computers and Communications

(2014) · doi:10.1109/isncc33829.2014 · Cited by 142

Incentivizing Differentially Private Federated Learning: A Multidimensional Contract Approach

Wu, M., Ye, D., Ding, J., Guo, Y., Yu, R., & Pan, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3050163 · Cited by 138

Variation-Aware Federated Learning With Multi-Source Decentralized Medical Image Data

Yan, Z., Wicaksana, J., Wang, Z., Yang, X., & Cheng, K.-T. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3040015 · Cited by 137

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

Duan, M., Liu, D., Ji, X., Wu, Y., Liang, L., Chen, X., Tan, Y., & Ren, A. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3134263 · Cited by 135

Amalgamation of Advanced Technologies for Sustainable Development of Smart City Environment: A Review

Rani, S., Mishra, R. K., Usman, M. J., Kataria, A., Kumar, P., Bhambri, P., & Mishra, A. K. (2021) · IEEE Access · doi:10.1109/access.2021.3125527 · Cited by 127

Artificial intelligence‐powered decentralized framework for Internet of Things in Healthcare 4.0

Puri, V., Kataria, A., & Sharma, V. (2021) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4245 · Cited by 126

Experiments of Federated Learning for COVID-19 Chest X-ray Images

Yan, B., Wang, J., Cheng, J., Zhou, Y., Zhang, Y., Yang, Y., Liu, L., Zhao, H., Wang, C., & Liu, B. (2021) · Communications in Computer and Information Science · doi:10.1007/978-3-030-78618-2_4 · Cited by 122

Cryptography Secure against Related-Key Attacks and Tampering

Mihir Bellare, Y., Cash, D., & Miller, R. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-25385-0_26 · Cited by 121

Secure and Efficient Federated Transfer Learning

Sharma, S., Xing, C., Liu, Y., & Kang, Y. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006280 · Cited by 120

A 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

Cyber Attacks on Healthcare Devices Using Unmanned Aerial Vehicles

Sethuraman, S. C., Vijayakumar, V., & Walczak, S. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1489-9 · Cited by 115

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

Zhang, L., Shen, B., Barnawi, A., Xi, S., Kumar, N., & Wu, Y. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10144-6 · Cited by 112

Two-stage Federated Phenotyping and Patient Representation Learning

Liu, D., Dligach, D., & Miller, T. A. (2019) · BioNLP Workshop and Shared Task (BioNLP) · doi:10.18653/v1/w19-5030 · Cited by 110

Federated Learning Meets Human Emotions: A Decentralized Framework for Human–Computer Interaction for IoT Applications

Chhikara, P., Singh, P., Tekchandani, R., Kumar, N., & Guizani, M. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3037207 · Cited by 109

COVID-19 detection using federated machine learning

Abdul Salam, M., Taha, S., & Ramadan, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0252573 · Cited by 109

Embedded Security in Cars

Lemke, K., Paar, C., & Wolf, M. (2006) · Springer eBooks · doi:10.1007/3-540-28428-1 · Cited by 106

Federated Learning: Challenges, Methods, and Future Directions

Singh, P., Singh, M. K., Singh, R., & Singh, N. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_13 · Cited by 106

Achieving Searchable and Privacy-Preserving Data Sharing for Cloud-Assisted E-Healthcare System

Xu, C., Wang, N., Zhu, L., Sharif, K., & Zhang, C. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2917186 · Cited by 105

Blockchain-based federated learning methodologies in smart environments

Li, D., Luo, Z., & Cao, B. (2021) · Cluster Computing · doi:10.1007/s10586-021-03424-y · Cited by 105

Emotion recognition using secure edge and cloud computing

Hossain, M. S., & Muhammad, G. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.040 · Cited by 104

Deep Learning Techniques for Biomedical and Health Informatics

(2019) · Studies in big data · doi:10.1007/978-3-030-33966-1 · Cited by 104

Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

(2022) · doi:10.1145/3560905 · Cited by 103

A Provably Secure and Lightweight Patient-Healthcare Authentication Protocol in Wireless Body Area Networks

Alzahrani, B. A., Irshad, A., Albeshri, A. A., & Alsubhi, K. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07237-x · Cited by 102

An investigation and comparison of machine learning approaches for intrusion detection in IoMT network

Binbusayyis, A., Alaskar, H., Vaiyapuri, T., & Dinesh, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04568-3 · Cited by 100

Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration

Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228

Edge Computing in Healthcare: Innovations, Opportunities, and Challenges

Rancea, A., Anghel, I. M., & Cioara, T. (2024) · Future Internet · doi:10.3390/fi16090329 · Cited by 238

Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks

Khan, I. A., Razzak, I., Pi, D., Khan, N., Hussain, Y., Li, B., & Kousar, T. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102002 · Cited by 147

Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey

Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review

Messinis, S., Temenos, N., Protonotarios, N. Ε., Rallis, I., Kalogeras, D., & Doulamis, N. D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108036 · Cited by 142

Federated Learning for Secure and Privacy‐Aware Internet of Medical Things: Taxonomy, Emerging Applications, Open Challenges, and Future Directions

Wani, R. U. Z., & Can, Ö. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70432 · Cited by 3

Federated Learning Application for Secure and Privacy-Aware Internet of Medical Things

Khatoon1, D. V. L. P. R. (2026) · International Journal of Engineering Research and Science & Technology

FEDERATED LEARNING FOR SECURE IOMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

Mitra, P., Karmakar, S., Sinha, S., Prasad, S., Das, S. P., & Mukherjee, T. (2026) · Iterative International Publishers eBooks · doi:10.58532/nbennuraisd14

Privacy-Preserving Federated Learning Framework for IoMT Smart Healthcare Applications

Manzoor, S. I., Jain, S., & Singh, Y. P. (2025) · International Conference on Electronics, AI and Computing (EAIC) · doi:10.1109/eaic66483.2025.11101398 · Cited by 4

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 70