EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme

New directions in cryptography

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Federated learning: Challenges, methods, and future directions

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Practical Secure Aggregation for Privacy-Preserving Machine Learning

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Federated Learning in Mobile Edge Networks: A Comprehensive Survey

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Using encryption for authentication in large networks of computers

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Towards Federated Learning at Scale: System Design

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Secret key agreement by public discussion from common information

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Exploiting Unintended Feature Leakage in Collaborative Learning

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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey

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A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions

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

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Overview of AI and communication for 6G network: fundamentals, challenges, and future research opportunities

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Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey

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Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

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Handling privacy-sensitive medical data with federated learning: Challenges and future directions

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Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

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Edge and Cloud Computing in Smart Cities

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A comprehensive survey on federated learning techniques for healthcare informatics

Dasaradharami Reddy, K., & Gadekallu, T. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/8393990 · Cited by 126

Blockchain-Based Two-Stage Federated Learning With Non-IID Data in IoMT System

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Reviewing Federated Machine Learning and Its Use in Diseases Prediction

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23042112 · Cited by 114

Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT

Singh, P., Gaba, G. S., Kaur, A., Hedabou, M., & Gurtov, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3186250 · Cited by 103

A Survey on Artificial Intelligence Aided Internet-of-Things Technologies in Emerging Smart Libraries

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Federated Learning for IoMT Applications: A Standardization and Benchmarking Framework of Intrusion Detection Systems

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Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects

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A Privacy-Preserving Medical Data Sharing Scheme Based on Blockchain

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On the Physical Layer Security of Federated Learning Based IoMT Networks

Ahmed, J., Nguyen, T. N., Ali, B. Q., Javed, M. A., & Mirza, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3173947 · Cited by 81

BSMD:A blockchain-based secure storage mechanism for big spatio-temporal data

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Federated Learning for Privacy Preservation of Healthcare Data From Smartphone-Based Side-Channel Attacks

Rehman, A., Razzak, I., & Xu, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3171852 · Cited by 72

Incentive Mechanisms for Smart Grid: State of the Art, Challenges, Open Issues, Future Directions

Bhattacharya, S., Chengoden, R., Srivastava, G., Alazab, M., Javed, A. R., Victor, N., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6020047 · Cited by 70

Blockchain and cloud computing-based secure electronic healthcare records storage and sharing

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FedComm: A Privacy-Enhanced and Efficient Authentication Protocol for Federated Learning in Vehicular Ad-Hoc Networks

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VOSA: Verifiable and Oblivious Secure Aggregation for Privacy-Preserving Federated Learning

Wang, Y., Zhang, A., Wu, S., & Yu, S. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3226508 · Cited by 69

FL-CTIF: A federated learning based CTI framework based on information fusion for secure IIoT

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Multi-objective optimization-based privacy in data mining

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A comprehensive review on deep learning algorithms: Security and privacy issues

Tayyab, M. A., Marjani, M., Jhanjhi, N. Z., Hashem, M., Usmani, R. S. A., & Qamar, F. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103297 · Cited by 50

Blockchain-Based Distributed Federated Learning in Smart Grid

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SVCA: Secure and Verifiable Chained Aggregation for Privacy-Preserving Federated Learning

Xia, Y., Liu, Y., Dong, S., Li, M., & Guo, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3363712 · Cited by 35

Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

Fan, H., Huang, C., & Liu, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3226245 · Cited by 32

Attribute-Centric and Synthetic Data Based Privacy Preserving Methods: A Systematic Review

Majeed, A. (2023) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp3030030 · Cited by 33

Hyper-Graph Attention Based Federated Learning Methods for Use in Mental Health Detection

Ahmed, U., Lin, J. C., & Srivastava, G. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172269 · Cited by 32

Federated Learning for Human Activity Recognition: Overview, Advances, and Challenges

Aouedi, O., Sacco, A., Khan, L. U., Nguyen, D. C., & Guizani, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3484228 · Cited by 42

Issues in federated learning: some experiments and preliminary results

Bhanbhro, J., Nisticò, S., & Palopoli, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-81732-0 · Cited by 33

E2EGI: End-to-End Gradient Inversion in Federated Learning

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Privacy Aware Internet of Medical Things Data Certification Framework on Healthcare Blockchain of 5G Edge

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A privacy-preserving federated learning with a secure collaborative for malware detection models using Internet of Things resources

Alamer, A. M. A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101015 · Cited by 31

DefendFL: A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks

Liu, J., Li, X., Liu, X., Zhang, H., Miao, Y., & Deng, R. H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3423397 · Cited by 29

Secure Federated Evolutionary Optimization—A Survey

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A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues

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Guest Editorial Federated Learning for Privacy Preservation of Healthcare Data in Internet of Medical Things and Patient Monitoring

Gadekallu, T. R., Alazab, M., Hemanth, D. J., & Wang, W. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3234604 · Cited by 24

Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

Fan, H., & Zhou, Z. (2023) · Mathematics · doi:10.3390/math11010214 · Cited by 23

FedCCW: a privacy-preserving Byzantine-robust federated learning with local differential privacy for healthcare

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GAN-Driven Data Poisoning Attacks and Their Mitigation in Federated Learning Systems

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Rough Fermatean fuzzy decision-based approach for modelling IDS classifiers in the federated learning of IoMT applications

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Differentially private enhanced permissioned blockchain for private data sharing in industrial IoT

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FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing

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FedPIA: Parameter Importance-Based Optimized Federated Learning to Efficiently Process Non-IID Data on Consumer Electronic Devices

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Differential Privacy-Preserving of Multi-Party Collaboration Under Federated Learning in Data Center Networks

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ESAFL: Efficient Secure Additively Homomorphic Encryption for Cross-Silo Federated Learning

Wu, J., Zhang, W., & Luo, F. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3541612 · Cited by 21

Group verifiable secure aggregate federated learning based on secret sharing

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Federated learning in healthcare: Recent progress and challenges

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A privacy-preserving federated learning protocol with a secure data aggregation for the Internet of Everything

Basudan, S. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.05.005 · Cited by 14

VCSA: Verifiable and collusion-resistant secure aggregation for federated learning using symmetric homomorphic encryption

Ming, Y., Wang, S., Wang, C., Liu, H., Deng, Y., Zhao, Y., & Feng, J. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103279 · Cited by 17

Federated learning‐based private medical knowledge graph for epidemic surveillance in internet of things

Wu, X., Gao, J., Bilal, M., Dai, F., Xu, X., Qi, L., & Dou, W. (2023) · Expert Systems · doi:10.1111/exsy.13372 · Cited by 14

OVP-FL: Outsourced Verifiable Privacy-Preserving Federated Learning

Li, S., Wei, X., & Wang, H. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3543601 · Cited by 13

Privacy preservation techniques through data lifecycle: A comprehensive literature survey

Madhusudhanan, S., & Jose, A. C. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104473 · Cited by 14

A comprehensive survey on federated learning for privacy preservation in digital healthcare applications

Kanauzia, R., Singh, M., Gulati, S., Singh, B. M., Arora, N., & Gola, K. K. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02673-2 · Cited by 16

Securing Federated Learning: Approaches, Mechanisms and Opportunities

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2024) · Electronics · doi:10.3390/electronics13183675 · Cited by 12

TPFL: Privacy-preserving personalized federated learning mitigates model poisoning attacks

Zuo, S., Xie, Y., Yao, H., & Zhijie, K. (2025) · Information Sciences · doi:10.1016/j.ins.2025.121901 · Cited by 12

BM-PDA: Blockchain Based Multifunctional Private-Preserving Data Aggregation for e-Health Systems

Wang, C., Yang, Q., Shen, J., Wu, Q. M. J., & He, D. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3598867 · Cited by 11

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

Khemaissia, R., Derdour, M., Ferrag, M. A., & Bouhamed, M. M. (2023) · Informatica · doi:10.31449/inf.v47i9.5081 · Cited by 11

Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning

Peng, K., Shen, X., Gao, L., Wang, B., & Lu, Y. (2023) · Entropy · doi:10.3390/e25081125 · Cited by 11

Robust and Lightweight Data Aggregation With Histogram Estimation in Edge-Cloud Systems

Su, Y., Li, J., Li, J., Su, Z., Meng, W., Yin, H., & Lu, R. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3352734 · Cited by 10

HSADR: A New Highly Secure Aggregation and Dropout-Resilient Federated Learning Scheme for Radio Access Networks With Edge Computing Systems

Wu, F., Li, X., Li, J., Vijayakumar, P., Gupta, B. B., & Arya, V. (2024) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2024.3441532 · Cited by 11

Copyright protection framework for federated learning models against collusion attacks

Luo, Y., Li, Y., Qin, S., Fu, Q., & Liu, J. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121161 · Cited by 10

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

Xia, Y., Liu, Y., Chen, J., Liang, Y., Khan, F., Alturki, R., & Wang, X. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3517739 · Cited by 9

Federated Learning for a Dynamic Edge: A Modular and Resilient Approach

Almeida, L., Teixeira, R., Baldoni, G., Antunes, M., & Aguiar, R. L. (2025) · Sensors · doi:10.3390/s25123812 · Cited by 10

A Post-quantum Secure Aggregation for Federated Learning

Yang, S., Chen, Y., Tu, S., & Yang, Z. (2022) · International Conference on Communication and Network Security (ICCNS) · doi:10.1145/3586102.3586120 · Cited by 12

Schnorr Approval-Based Secure and Privacy-Preserving IoV Data Aggregation

Liu, R., & Pan, J. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3546040 · Cited by 8

A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward

Majeed, A., & Hwang, S. O. (2024) · IEEE Access · doi:10.1109/access.2024.3413069 · Cited by 12

Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework

Ayache, M., Asri, I. E., Al-Karaki, J. N., Bellouch, M., Gawanmeh, A., & Tazzi, K. (2023) · Annals of Telecommunications · doi:10.1007/s12243-023-00965-8 · Cited by 8

DWAMA: Dynamic weight-adjusted mahalanobis defense algorithm for mitigating poisoning attacks in federated learning

Zhang, G., Liu, H., Yang, B. T., & Feng, S. (2024) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-024-01794-9 · Cited by 7

LVSA: Lightweight and verifiable secure aggregation for federated learning

Li, G., Zhang, Z., & Du, R. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130712 · Cited by 7

Private Data Preprocessing for Privacy-preserving Federated Learning

Hsu, R., & Huang, T.-Y. (2022) · IEEE International Conference on Knowledge Innovation and Invention (ICKII) · doi:10.1109/ickii55100.2022.9983518 · Cited by 6

Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning: A Review

Peng, J., Guo, J., Bao, F., Yang, C., Xu, Y., & Qin, Y. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.041897 · Cited by 6

Research on the Privacy Protection Technology of Blockchain Spatiotemporal Big Data

Zhou, B., Zhao, J., Yin, Y., & Chen, G. (2024) · IEEE Access · doi:10.1109/access.2024.3415590 · Cited by 5

SAPD: Secure Authentication Protocol Development for Smart Healthcare Management Using IoT

Rahaman, M., Lin, C., & Moslehpour, M. (2023) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce59613.2023.10315475 · Cited by 5

A Review of Solving Non-IID Data in Federated Learning: Current Status and Future Directions

Lu, W., Cheng, J., Li, X., & He, J. C. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1277-9_5 · Cited by 6

An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

Shen, C., Zhang, W., Zhou, T., Zhang, Y., & Zhang, L. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.054434 · Cited by 9

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G

Abdulqadder, I. H., Aziz, I. T., & Zou, D. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110841 · Cited by 6

Privacy-Enhanced Federated WiFi Sensing for Health Monitoring in Internet of Things

Lian, Z., Zeng, Q., Liu, Z., Wang, H., Ma, C., Meng, W., Su, C., & Sakurai, K. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3476149 · Cited by 5

A Survey on Secure Aggregation for Privacy-Preserving Federated Learning

Chouhan, A., & Purushothama, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59100-6_2 · Cited by 5

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

An image watermark removal method for secure internet of things applications based on federated learning

Li, H.-A., Wang, G., Hua, Q., Wen, Z., Li, Z., & Lei, T. (2022) · Expert Systems · doi:10.1111/exsy.13036 · Cited by 4

Privacy-Preserving Object Detection With Poisoning Recognition for Autonomous Vehicles

Li, J., Guo, W., Xie, L., Liu, X., & Cai, J. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3227119 · Cited by 4

An Aggregation Protocol Resisting Collusion Attacks in the Internet of Vehicles Environment

Xu, Z., Zhang, R., Huang, P., & Xu, J. (2023) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/cscloud-edgecom58631.2023.00014 · Cited by 4

A verifiable privacy-preserving federated learning scheme based on homomorphic proxy re-encryption

Song, Z., Huang, Z., & Cai, Y. (2025) · Information Sciences · doi:10.1016/j.ins.2025.122875 · Cited by 3

Clustered federated learning with heterogeneous differential privacy on Non-IID data

Guo, P., Bai, C. G., Zhang, M., & Dusadeerungsikul, P. O. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108339 · Cited by 5

Hierarchical Swarm Learning for Edge-Assisted Collaborative Vehicle Trajectory Prediction

Hou, X., Chen, L., Tang, J., Li, J., & Yang, W. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278931 · Cited by 3

SSL-FL: A lightweight authentication framework for secure federated learning

Gao, X., Chen, B. Y., Yao, X. H., Yuan, Y., & Suo, Z. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00029-y · Cited by 3

Fault-Tolerant Federated Learning Framework for Edge Devices in Unstable NetworksPreprint

Myakala, P. K., & Agrawal, M. (2025) · doi:10.36227/techrxiv.174612128.82578829/v1 · Cited by 3

CAPP : Context‐Aware Privacy‐Preserving Continuous Authentication in Smartphones

Jacob, S., Vinod, P., & Menon, V. G. (2024) · Security and Privacy · doi:10.1002/spy2.478 · Cited by 2

RobustPPFL: A Secure and Robust Privacy-Preserving Federated Learning Framework Against Poisoning Attacks

Xue, K., Li, J., Xue, R., Xue, Y., & Zhao, J. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3632902 · Cited by 3

Intelligent web archiving and ranking of fake news using metadata-driven credibility assessment and machine learning

Abrar, M. F., Saqib, M., Alferaidi, A., Almuraziq, T. S., Uddin, R., Khan, W., & Khan, Z. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-31583-0 · Cited by 2

PolyFLAG_SVM: a Polymorphic Federated Learning Aggregation of Gradients Support Vector Machines Framework

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.09.021 · Cited by 2

Recent advances in federated learning for digital healthcare systems

Mohnani, P., Thümmler, C., Castillo, A. A., Tolba, R., Bassi, A., Simon, A., Gavras, A., Toscano, O., & Haigron, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13897-3.00005-9 · Cited by 2

HP_FLAP: homomorphic and polymorphic federated learning aggregation of parameters framework

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2025) · Cybersecurity · doi:10.1186/s42400-024-00341-6 · Cited by 3

A Reputation-Based Learning Nodes Selection Algorithm for Decentralized Federated Learning

Qu, J., Feng, Y., Xu, H., & Xie, K. (2023) · IEEE International Conference on Electronic Information and Communication Technology (ICEICT) · doi:10.1109/iceict57916.2023.10245743 · Cited by 1

Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation

Boenisch, F., Dziedzic, A., Schuster, R., Shamsabadi, A. S., Shumailov, I., & Papernot, N. (2023) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp57164.2023.00023 · Cited by 38

EvaFL: An Efficient Verifiable Privacy-Preserving Federated Learning Against Malicious Servers

Yang, X., Zou, X., Zhao, X., Chen, Q., Qin, B., Zhao, Y., & Yu, Y. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3688036

PRFL:Privacy-preserving Robust Aggregation Method for Federated Learning

GAO Qi, S. Y. (2024) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.11896/jsjkx.231000158

DPAD: Data Poisoning Attack Defense Mechanism for federated learning-based system

Basak, S., & Chatterjee, K. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109893 · Cited by 16

Publicly Verifiable and Fault-Tolerant Privacy-Preserving Aggregation for Federated Learning

Li, G., Jiang, Q. G., Zhou, L., & Yang, L. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761110 · Cited by 1