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

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Learning Multiple Layers of Features from Tiny Images

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Communication-Efficient Learning of Deep Networks from Decentralized Data

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MIMIC-III, a freely accessible critical care database

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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)

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Federated Machine Learning: Concept and Applications

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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 MovieLens Datasets: History and Context

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Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

Khan, L. U., Saad, W., Han, Z., Hossain, E., & Hong, C. S. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3090430 · Cited by 892

Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems

Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 506

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Ghimire, B., & Rawat, D. B. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3150363 · Cited by 520

A state-of-the-art survey on solving non-IID data in Federated Learning

Ma, X., Zhu, J., Lin, Z., Chen, S., & Qin, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.003 · Cited by 435

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

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

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Fu, L., Zhang, H., Gao, G., Zhang, M., & Liu, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299573 · Cited by 343

A Review of Federated Learning Methods in Heterogeneous Scenarios

Pei, J., Liu, W., Li, J., Wang, L., & Liu, C. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3385440 · Cited by 307

Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm

Leng, J., Zhong, Y., Lin, Z., Xu, K., Mourtzis, D., Zhou, X., Zheng, P., Liu, Q., Zhao, J. L., & Shen, W. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.08.023 · Cited by 270

Evaluating Federated Learning for intrusion detection in Internet of Things: Review and challenges

Campos, E. M., Saura, P. F., González-Vidal, A., Hernández-Ramos, J. L., Bernabé, J. B., Baldini, G., & Skármeta, A. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108661 · Cited by 294

Vulnerabilities in federated learning

Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248

Federated 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

TFL-DT: A Trust Evaluation Scheme for Federated Learning in Digital Twin for Mobile Networks

Guo, J., Liu, Z., Tian, S., Huang, F., Li, J., Li, X., Kostromitin, K. I., & Ma, J. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3310094 · Cited by 223

A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions

Arisdakessian, S., Wahab, O. A., Mourad, A., Otrok, H., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3203249 · Cited by 227

Poisoning attacks in federated learning: A survey

Xia, G., Chen, J., Yu, C., & Ma, J. (2023) · IEEE Access · doi:10.1109/access.2023.3238823 · Cited by 212

Electricity Consumer Characteristics Identification: A Federated Learning Approach

Wang, Y., Bennani, I. L., Liu, X., Sun, M., & Zhou, Y. (2021) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2021.3066577 · Cited by 192

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3200740 · Cited by 223

Federated learning for the healthcare metaverse: Concepts, applications, challenges, and future directions

Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

Hu, S., Chen, X., Ni, W., Hossain, E., & Wang, X. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3086014 · Cited by 190

Securing federated learning with blockchain: a systematic literature review

Qammar, A., Karim, A., Ning, H., & Ding, J. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10271-9 · Cited by 193

Communication and computation efficiency in Federated Learning: A survey

Almanifi, O. R. A., Chow, C., Tham, M., Chuah, J. H., & Kanesan, J. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100742 · Cited by 180

Combining Federated Learning and Edge Computing Toward Ubiquitous Intelligence in 6G Network: Challenges, Recent Advances, and Future Directions

Duan, Q., Huang, J., Hu, S., Deng, R., Lu, Z., & Yu, S. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3316615 · Cited by 185

Semi-Decentralized Federated Learning With Cooperative D2D Local Model Aggregations

Lin, F., Hosseinalipour, S., Azam, S. S., Brinton, C. G., & Michelusi, N. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3118344 · Cited by 152

Decentralized Federated Learning via Mutual Knowledge Transfer

Li, C., Li, G., & Varshney, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078543 · Cited by 139

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130

Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions

Javeed, D., Saeed, M. S., Ahmad, I., Adil, M., Kumar, P., & Islam, A. K. M. N. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.023 · Cited by 143

Federated Learning-Based Explainable Anomaly Detection for Industrial Control Systems

Hương, T. T., Bac, T. P., Ha, K. N., Việt, N. H., Hoang, N. X., Hung, N. T., & Tran, K. P. (2022) · IEEE Access · doi:10.1109/access.2022.3173288 · Cited by 126

Federated Learning for Computationally Constrained Heterogeneous Devices: A Survey

Pfeiffer, K., Rapp, M., Khalili, R., & Henkel, J. (2023) · ACM Computing Surveys · doi:10.1145/3596907 · Cited by 125

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

Al-Quraan, M. M., Mohjazi, L., Bariah, L., Centeno, A., Zoha, A., Arshad, K., Assaleh, K. T., Muhaidat, S., Debbah, M. A., & Imran, M. A. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3251404 · Cited by 159

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

Alhashimi, H. F., Hindia, M. N., Dimyati, K., Hanafi, E., Safie, N., Qamar, F., Azrin, K., & Nguyễn, Q. N. (2023) · Electronics · doi:10.3390/electronics12030647 · Cited by 117

Fairness and privacy preserving in federated learning: A survey

Rafi, T. H., Noor, F. A., Hussain, T., & Chae, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102198 · Cited by 138

Federated Learning With Soft Clustering

Li, C., Li, G., & Varshney, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3113927 · Cited by 126

A Survey on Mobility of Edge Computing Networks in IoT: State-of-the-Art, Architectures, and Challenges

Abkenar, F. S., Ramezani, P., Iranmanesh, S., Murali, S., Chulerttiyawong, D., Wan, X., Jamalipour, A., & Raad, R. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3211462 · Cited by 116

Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Khraisat, A., Alazab, A., Singh, S., Jan, T., & Gomez, A. J. (2024) · ACM Computing Surveys · doi:10.1145/3687124 · Cited by 152

Federated 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 126

Federated learning for medical imaging radiology

Rehman, M. H. U., Pinaya, W. H. L., Nachev, P., Teo, J., Sebastien Ourselin, A., & Cardoso, M. J. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220890 · Cited by 125

A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data

Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107630 · Cited by 103

Privacy-Preserving Data-Driven Learning Models for Emerging Communication Networks: A Comprehensive Survey

Fouda, M. M., Fadlullah, Z. M., Ibrahem, M. I., & Kato, N. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3486690 · Cited by 103

Hybrid Classifier-Based Federated Learning in Health Service Providers for Cardiovascular Disease Prediction

Yaqoob, M. M., Nazir, M., Khan, M. A., Qureshi, S., & Al-Rasheed, A. (2023) · Applied Sciences · doi:10.3390/app13031911 · Cited by 96

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail, L., & Buyya, R. (2022) · Sensors · doi:10.3390/s22155750 · Cited by 103

Personalized Federated Learning With Adaptive Batchnorm for Healthcare

Wang, L., Wang, J., Chen, Y., Qin, X., Xu, R., Dimitriadis, D., & Qin, T. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3177197 · Cited by 101

A comprehensive survey on 6G and beyond: Enabling technologies, opportunities of machine learning and challenges

Jawad, A. T., Mâaloul, R., & Chaari, L. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.110085 · Cited by 105

Exploring Deep-Reinforcement-Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

Zheng, J., Li, K., Mhaisen, N., Ni, W., Tovar, E., & Guizani, M. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3176739 · Cited by 90

Trustworthy federated learning: privacy, security, and beyond

Chen, C., Liu, J., Tan, H., Li, X., Wang, K. I., Li, P., Sakurai, K., & Dou, D. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02285-2 · Cited by 110

A Comprehensive Survey of Digital Twins and Federated Learning for Industrial Internet of Things (IIoT), Internet of Vehicles (IoV) and Internet of Drones (IoD)

Jamil, S., Rahman, M., & Fawad, F. (2022) · Applied System Innovation · doi:10.3390/asi5030056 · Cited by 96

A Secure Federated Learning Framework for Residential Short-Term Load Forecasting

Husnoo, M. A., Anwar, A., Hosseinzadeh, N., Islam, S. N., Mahmood, A. N., & Doss, R. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3292382 · Cited by 90

FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

Bebortta, S., Tripathy, S. S., Basheer, S., & Chowdhary, C. L. (2023) · Diagnostics · doi:10.3390/diagnostics13203166 · Cited by 95

Privacy-Preserving Brain–Computer Interfaces: A Systematic Review

Xia, K., Duch, W., Sun, Y., Xu, K., Fang, W., Luo, H., Zhang, Y., Sang, D., Xu, X., Wang, F., et al. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3184818 · Cited by 95

Multicenter Hierarchical Federated Learning With Fault-Tolerance Mechanisms for Resilient Edge Computing Networks

Chen, X., Xu, G., Xu, X., Jiang, H., Tian, Z., & Ma, T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3362974 · Cited by 87

Privacy-preserving in Blockchain-based Federated Learning systems

Sameera, K. M., Nicolazzo, S., Arazzi, M., Nocera, A., A., R. R. K., Vinod, P., & Conti, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.04.024 · Cited by 107

Federated learning-empowered smart manufacturing and product lifecycle management: A review

Leng, J., Li, R. C., Xie, J., Zhou, X., Li, X., Liu, Q., Chen, X., Shen, W., & Wang, L. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103179 · Cited by 88

Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics

Mohammadi, S., Balador, A., Sinaei, S., & Flammini, F. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104918 · Cited by 93

A federated learning method for network intrusion detection

Tang, Z., Hu, H., & Xu, C. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6812 · Cited by 89

Federated Learning and Its Role in the Privacy Preservation of IoT Devices

Alam, T., & Gupta, R. (2022) · Future Internet · doi:10.3390/fi14090246 · Cited by 84

Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

Nguyen, C. T., Saputra, Y. M., Huynh, N. V., Nguyen, T. N., Hoang, D. T., Nguyen, D. N., Pham, V., Vozn̆ák, M., Chatzinotas, S., & Tran, D.-H. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3418574 · Cited by 79

Privacy-Preserving Blockchain-Based Federated Learning for Marine Internet of Things

Qin, Z., Jin, Y., Meng, J., Lu, B., & Wang, L. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3100258 · Cited by 78

AI in Pathology: What could possibly go wrong?

Nakagawa, K., Moukheiber, L., Celi, L. A., Patel, M. P., Mahmood, F., Gondim, D. D., Hogarth, M. A., & Levenson, R. M. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.006 · Cited by 79

A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life

Kamei, S., & Taghipour, S. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2023.109130 · Cited by 82

Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: literature review

Orabi, M. M., Emam, O. E., & Fahmy, H. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01099-5 · Cited by 77

An explainable federated learning and blockchain-based secure credit modeling method

Yang, F., Abedin, M. Z., & Hájek, P. (2023) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.08.040 · Cited by 76

Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey

Le, M., Huynh‐The, T., Do‐Duy, T., Vu, T.-H., Hwang, W., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3427324 · Cited by 94

A survey on federated learning in data mining

Yu, B., Mao, W., Lv, Y., Zhang, C. B. C., & Xie, Y. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1443 · Cited by 78

Machine Learning in Network Slicing—A Survey

Phyu, H. P., Naboulsi, D., & Stanica, R. (2023) · IEEE Access · doi:10.1109/access.2023.3267985 · Cited by 75

A Review on Intelligent Energy Management Systems for Future Electric Vehicle Transportation

Teimoori, Z., & Yassine, A. (2022) · Sustainability · doi:10.3390/su142114100 · Cited by 72

UAV-Assisted Online Machine Learning Over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

Wang, S., Hosseinalipour, S., Gorlatova, M. A., Brinton, C. G., & Chiang, M. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3216326 · Cited by 73

Enhancing Communication Networks in the New Era with Artificial Intelligence: Techniques, Applications, and Future Directions

El-Hajj, M. I. (2025) · Network · doi:10.3390/network5010001 · Cited by 95

Blockchain-Based Federated Learning: A Survey and New Perspectives

Ning, W., Zhu, Y., Song, C., Li, H., Zhu, L., Xie, J., Chen, T., Xu, T., Xi, X., & Gao, J. (2024) · Applied Sciences · doi:10.3390/app14209459 · Cited by 78

A scoping review of the governance of federated learning in healthcare

Eden, R., Chukwudi, I., Bain, C., Barbieri, S., Callaway, L. K., de Jersey, S., George, Y., Gorse, A., Lawley, M., Marendy, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01836-3 · Cited by 88

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu, T. M., Kim, T., & Lin, J.-W. (2024) · Sensors · doi:10.3390/s24030968 · Cited by 82

Intrusion Detection Based on Federated Learning: A Systematic Review

Hernández-Ramos, J. L., Καρόπουλος, Γ., Chatzoglou, E., Kouliaridis, V., Campos, E. M., González-Vidal, A., & Kambourakis, G. (2025) · ACM Computing Surveys · doi:10.1145/3731596 · Cited by 117

Federated FCM: Clustering Under Privacy Requirements

Pedrycz, W. (2021) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2021.3105193 · Cited by 67

The Internet of Federated Things (IoFT)

Kontar, R. A., Shi, N., Yue, X., Chung, S., Byon, E., Chowdhury, M., Jin, J. J., Kontar, W., Masoud, N., Nouiehed, M., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3127448 · Cited by 72

Federated Learning for Decentralized DDoS Attack Detection in IoT Networks

Alhasawi, Y., & Alghamdi, S. (2024) · IEEE Access · doi:10.1109/access.2024.3378727 · Cited by 69

Cybersecurity in process control, operations, and supply chain

Parker, S., Wu, Z., & Christofides, P. D. (2023) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2023.108169 · Cited by 68

A survey for federated learning evaluations: Goals and measures

Chai, D., Wang, L., Yang, L., Zhang, J., Chen, K., & Yang, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3382002 · Cited by 64

A Detailed Survey on Federated Learning Attacks and Defenses

Sikandar, H. S., Waheed, H., Tahir, S., Malik, S. U. R., & Rafique, W. (2023) · Electronics · doi:10.3390/electronics12020260 · Cited by 64

Decentralized Aggregation for Energy-Efficient Federated Learning via D2D Communications

Al-Abiad, M. S., Obeed, M., Hossain, M., & Chaaban, A. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3253718 · Cited by 52

On the feasibility of Federated Learning towards on-demand client deployment at the edge

Chahoud, M., Otoum, S., & Mourad, A. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103150 · Cited by 61

Hospital patients’ length of stay prediction: A federated learning approach

Rahman, M. M., Kundu, D., Suha, S. A., Siddiqi, U. R., & Dey, S. K. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.07.006 · Cited by 66

Privacy-Preserving Federated Learning Against Label-Flipping Attacks on Non-IID Data

Shen, X., Liu, Y., Li, F., & Li, C. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288886 · Cited by 62

Network-Aware Optimization of Distributed Learning for Fog Computing

Wang, S., Ruan, Y., Tu, Y., Wagle, S., Brinton, C. G., & Joe‐Wong, C. (2021) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2021.3075432 · Cited by 59

Adaptive client selection with personalization for communication efficient Federated Learning

Souza, A. M. D., Maciel, F., da Costa, J. B. D., Bittencourt, L. F., Cerqueira, E., Loureiro, A. A. F., & Villas, L. A. (2024) · Ad Hoc Networks · doi:10.1016/j.adhoc.2024.103462 · Cited by 58

Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Yaqoob, M. M., Nazir, M., Yousafzai, A., Khan, M. A., Shaikh, A. A., Algarni, A. D., & Elmannai, H. (2022) · Applied Sciences · doi:10.3390/app122312080 · Cited by 57

Over-the-Air Decentralized Federated Learning

Shi, Y.-D., Zhou, Y., & Shi, Y.-M. (2021) · International Symposium on Information Theory · doi:10.1109/isit45174.2021.9517780 · Cited by 54

FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection

Nguyen, V. T., & Beuran, R. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104337 · Cited by 54

Securing the Industrial Internet of Things against ransomware attacks: A comprehensive analysis of the emerging threat landscape and detection mechanisms

Al-Hawawreh, M. S., Alazab, M., Ferrag, M. A., & Hossain, M. S. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103809 · Cited by 53

Secure federated learning for Alzheimer's disease detection

Mitrovska, A., Safari, P., Ritter, K., Shariati, B., & Fischer, J. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1324032 · Cited by 61

Visual privacy attacks and defenses in deep learning: a survey

Zhang, G., Liu, B., Zhu, T., Zhou, A., & Zhou, W. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10123-y · Cited by 56

Applicability of Deep Reinforcement Learning for Efficient Federated Learning in Massive IoT Communications

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