Threats to Federated Learning

Communication-Efficient Learning of Deep Networks from Decentralized Data

McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215

Intriguing properties of neural networks

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143

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

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578

Federated Machine Learning: Concept and Applications

Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322

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

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961

Deep Leakage from Gradients

Zhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906

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

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

Federated learning on non-IID data: A survey

Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062

A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges

Xu, M., Ng, W. C., Lim, W. Y. B., Kang, J., Xiong, Z., Niyato, D. T., Yang, Q., Shen, X., & Miao, C. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3221119 · Cited by 614

Advances, challenges and opportunities in creating data for trustworthy AI

Liang, W., Tadesse, G. A., Ho, D. E., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00516-1 · Cited by 617

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548

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

From distributed machine learning to federated learning: a survey

Liu, J., Huang, J., Zhou, Y., Li, X., Ji, S., Xiong, H., & Dou, D. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01664-x · Cited by 397

Federated Learning in Edge Computing: A Systematic Survey

Abreha, H. G., Hayajneh, M., & Serhani, M. A. (2022) · Sensors · doi:10.3390/s22020450 · Cited by 384

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3162397 · Cited by 362

Multi-center federated learning: clients clustering for better personalization

Long, G., Xie, M. X., Shen, T., Zhou, T., Wang, X., & Jiang, J. B. (2022) · World Wide Web · doi:10.1007/s11280-022-01046-x · Cited by 399

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

Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey

Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2022) · ACM Computing Surveys · doi:10.1145/3560816 · Cited by 347

Decentralized Federated Learning: A Survey and Perspective

Yuan, L., Wang, Z., Sun, L., Yu, P. S., & Brinton, C. G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3407584 · Cited by 365

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

Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges

Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296

Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives

Liu, P., Xu, X., & Wang, W. (2022) · Cybersecurity · doi:10.1186/s42400-021-00105-6 · Cited by 261

Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark

Huang, W., Ye, M., Shi, Z., Wan, G., He, L., Du, B., & Yang, Q. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3418862 · Cited by 280

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Qu, L., Zhou, Y., Liang, P. P., Xia, Y., Wang, F., Adeli, E., Fei-Fei, L., & Rubin, D. L. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00982 · Cited by 235

Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges

Zheng, Z., Zhou, Y., Sun, Y., Wang, Z., Liu, B., & Li, K. (2021) · Connection Science · doi:10.1080/09540091.2021.1936455 · Cited by 221

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

Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey

Li, D., Han, D., Weng, T., Zheng, Z., Li, H., Liu, H., Castiglione, A., & Li, K. (2021) · Soft Computing · doi:10.1007/s00500-021-06496-5 · Cited by 221

Collaborative Fairness in Federated Learning

Lyu, L., Xu, X., Wang, Q., & Yu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63076-8_14 · Cited by 200

Decentralized Federated Learning: A Survey on Security and Privacy

Hallaji, E., Razavi‐Far, R., Saif, M., Wang, B., & Yang, Q. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3362191 · Cited by 204

A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Boualouache, A., & Engel, T. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3236448 · Cited by 179

Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges

Ali, M., Karimipour, H., & Tariq, M. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102355 · Cited by 177

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 189

Privacy and Security in Federated Learning: A Survey

Gosselin, R., Vieu, L., Loukil, F., & Benoît, A. (2022) · Applied Sciences · doi:10.3390/app12199901 · Cited by 178

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

Ji, S., Tan, Y., Saravirta, T., Yang, Z. Q., Liu, Y., Vasankari, L., Pan, S., Long, G., & Walid, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02119-1 · Cited by 192

Edge Learning for 6G-Enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

Ferrag, M. A., Friha, O., Kantarcı, B., Tihanyi, N., Cordeiro, L. C., Debbah, M. A., Hamouda, D., Al-Hawawreh, M. S., & Choo, K. R. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3317242 · Cited by 185

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

Aouedi, O., Sacco, A., Piamrat, K., & Marchetto, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3185673 · Cited by 165

Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges

Pouriyeh, S., Shahid, O., Parizi, R. M., Sheng, Q. Z., Srivastava, G., Zhao, L., & Nasajpour, M. (2022) · Applied Sciences · doi:10.3390/app12188980 · Cited by 200

Reviewing Federated Learning Aggregation Algorithms; Strategies, Contributions, Limitations and Future Perspectives

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Electronics · doi:10.3390/electronics12102287 · Cited by 176

Towards Fairness-Aware Federated Learning

Shi, Y., Yu, H., & Leung, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3263594 · Cited by 163

An overview of implementing security and privacy in federated learning

Hu, K., Gong, S., Zhang, Q., Seng, C., Xia, M., & Jiang, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10846-8 · Cited by 175

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

Song, J., Wang, W., Gadekallu, T. R., Cao, J., & Liu, Y. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3153519 · Cited by 150

Dataset Distillation: A Comprehensive Review

Yu, R., Liu, S., & Wang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3323376 · Cited by 200

Challenges, Applications and Design Aspects of Federated Learning: A Survey

Rahman, K. M. J., Ahmed, F., Akhter, N., Hasan, M., Amin, R., Aziz, K. E., Islam, A. K. M. M., Mukta, M. S. H., & Islam, A. K. M. N. (2021) · IEEE Access · doi:10.1109/access.2021.3111118 · Cited by 147

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning

Wang, Z., Hu, Q., Li, R., Xu, M., & Xiong, Z. (2023) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2023.3253604 · Cited by 141

Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm

Aziz, R., Banerjee, S., Bouzefrane, S., & Vinh, T. L. (2023) · Future Internet · doi:10.3390/fi15090310 · Cited by 149

A privacy preserving framework for federated learning in smart healthcare systems

Wang, W., Li, X., Qiu, X., Zhang, X., Brusić, V., & Zhao, J. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103167 · Cited by 145

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

A Decentralized Federated Learning Framework via Committee Mechanism With Convergence Guarantee

Che, C., Li, X., Chen, C., He, X., & Zheng, Z. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3202887 · Cited by 134

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

Brauneck, A., Schmalhorst, L., Kazemi Majdabadi, M. M., Bakhtiari, M., Völker, U., Baumbach, J., Baumbach, L., & Buchholtz, G. (2023) · Journal of Medical Internet Research · doi:10.2196/41588 · Cited by 130

An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning

Li, S., Ngai, E. C. ‐., & Voigt, T. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3237397 · Cited by 126

PVD-FL: A Privacy-Preserving and Verifiable Decentralized Federated Learning Framework

Zhao, J., Zhu, H., Wang, F., Lu, R., Liu, Z., & Li, H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3176191 · Cited by 120

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

Chen, C., Zhou, J., Zheng, L., Wu, H., Lyu, L., Wu, J. X., Wu, B., Liu, Z., Wang, L., & Zheng, X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/272 · Cited by 149

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

FedIPR: Ownership Verification for Federated Deep Neural Network Models

Li, B., Fan, L., Gu, H., Li, J., & Yang, Q. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3195956 · Cited by 115

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

Trustworthy AI: Securing Sensitive Data in Large Language Models

Feretzakis, G., & Verykios, V. S. (2024) · AI · doi:10.3390/ai5040134 · Cited by 102

MaskCrypt: Federated Learning With Selective Homomorphic Encryption

Hu, C., & Li, B. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3392424 · Cited by 98

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Saha, S., Hota, A., Chattopadhyay, A. K., Nag, A., & Nandi, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10766-7 · Cited by 94

A comprehensive survey of federated transfer learning: challenges, methods and applications

Guo, W., Zhuang, F., Zhang, X., Tong, Y., & Dong, J. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40065-x · Cited by 95

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

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

Patel, V. A., Bhattacharya, P., Tanwar, S., Gupta, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3201876 · Cited by 88

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Qiao, C., Li, M., Liu, Y., & Tian, Z. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3399612 · Cited by 92

A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness and Privacy

Zhang, Y., Zeng, D., Luo, J., Xu, Z., & King, I. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543873.3587681 · Cited by 89

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

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

Federated Learning with Privacy-preserving and Model IP-right-protection

Yang, Q., Huang, A., Fan, L., Chan, C. S., Lim, J. H., Ng, K. W., Ong, D. S., & Li, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1343-2 · Cited by 88

Federated Quantum-Based Privacy-Preserving Threat Detection Model for Consumer Internet of Things

Namakshenas, D., Yazdinejad, A., Dehghantanha, A., & Srivastava, G. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3377550 · Cited by 75

Comparative Review of the Intrusion Detection Systems Based on Federated Learning: Advantages and Open Challenges

Владимировна, Ф. Е., Novikova, E. S., & Shulepov, A. (2022) · Algorithms · doi:10.3390/a15070247 · Cited by 74

Federated Learning as a Privacy Solution - An Overview

Khan, M., Glavin, F. G., & Nickles, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.227 · Cited by 75

Federated Learning for Enhanced Cybersecurity and Trustworthiness in 5G and 6G Networks: A Comprehensive Survey

Blika, A., Palmos, S., Doukas, G. S., Lamprou, V., Pelekis, S., Kontoulis, M., Ntanos, C., & Askounis, D. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3449563 · Cited by 82

Partially Encrypted Multi-Party Computation for Federated Learning

Sotthiwat, E., Zhen, L., Li, Z., & Zhang, C. (2021) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid51090.2021.00101 · Cited by 69

FedLess: Secure and Scalable Federated Learning Using Serverless Computing

Grafberger, A., Chadha, M., Jindal, A., Gu, J., & Gerndt, M. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672067 · Cited by 74

Federated Learning Attacks and Defenses: A Survey

Chen, Y., Gui, Y., Lin, H., Gan, W., & Wu, Y. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020431 · Cited by 69

Federated Contrastive Learning for Volumetric Medical Image Segmentation

Wu, Y.-W., Zeng, D., Wang, Z., Shi, Y.-Y., & Hu, J. (2022) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-030-87199-4_35 · Cited by 77

ADFL: Defending backdoor attacks in federated learning via adversarial distillation

Zhu, C., Zhang, J., Sun, X., Chen, B., & Meng, W. (2023) · Computers & Security · doi:10.1016/j.cose.2023.103366 · Cited by 61

An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies

Enthoven, D., & Al-Ars, Z. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_8 · Cited by 72

Enabling All In-Edge Deep Learning: A Literature Review

Joshi, P., Hasanuzzaman, M., Thapa, C., Afli, H., & Scully, T. (2023) · IEEE Access · doi:10.1109/access.2023.3234761 · Cited by 67

A comprehensive review of federated learning for COVID‐19 detection

Naz, S., Phan, K. T., & Chen, Y. P. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22777 · Cited by 59

Federated learning design and functional models: survey

Ayeelyan, J., Utomo, S., Rouniyar, A., Hsu, H.-C., & Hsiung, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10969-y · Cited by 56

Natural Language Understanding with Privacy-Preserving BERT

Qu, C., Kong, W., Yang, L., Zhang, M., Bendersky, M., & Najork, M. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482281 · Cited by 73

Federated learning attack surface: taxonomy, cyber defences, challenges, and future directions

Qammar, A., Ding, J., & Ning, H. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10098-w · Cited by 58

A systematic review of federated learning from clients’ perspective: challenges and solutions

Shanmugarasa, Y., Paik, H.-Y., Kanhere, S. S., & Zhu, L. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10563-8 · Cited by 55

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Mosaiyebzadeh, F., Pouriyeh, S., Parizi, R. M., Sheng, Q. Z., Han, M., Zhao, L., Sannino, G., Ranieri, C. M., Ueyama, J., & Batista, D. M. (2023) · Electronics · doi:10.3390/electronics12122703 · Cited by 51

ShareChain : Blockchain‐enabled model for sharing patient data using federated learning and differential privacy

Javed, L., Anjum, A., Yakubu, B. M., Iqbal, M., Moqurrab, S. A., & Srivastava, G. (2022) · Expert Systems · doi:10.1111/exsy.13131 · Cited by 49

Emerging AI threats in cybercrime: a review of zero-day attacks via machine, deep, and federated learning

Alansary, S. A., Ayyad, S. M., Talaat, F. M., & Saafan, M. M. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02556-6 · Cited by 54

Adaptive Clustering Based Personalized Federated Learning Framework for Next POI Recommendation With Location Noise

Ye, Z., Zhang, X., Chen, X., Xiong, H., & Yu, D. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3312511 · Cited by 51

A Survey on Blockchain-Based Federated Learning

Wu, L., Ruan, W., Hu, J., & He, Y. (2023) · Future Internet · doi:10.3390/fi15120400 · Cited by 48

Toward Efficient and Certified Recovery From Poisoning Attacks in Federated Learning

Jiang, Y., Shen, J., Liu, Z., Tan, C. W., & Lam, K. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3533907 · Cited by 48

Untargeted Poisoning Attack Detection in Federated Learning via Behavior AttestationAl

Mallah, R. A., Lopez, D. L., Badu-Marfo, G., & Farooq, B. (2023) · IEEE Access · doi:10.1109/access.2023.3330144 · Cited by 53

Source Inference Attacks: Beyond Membership Inference Attacks in Federated Learning

Hu, H., Zhang, X., Salcic, Z. A., Sun, L., Choo, K. R., & Dobbie, G. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3321565 · Cited by 47

Toward Federated-Learning-Enabled Visible Light Communication in 6G Systems

Naser, S. A., Bariah, L., Muhaidat, S., Sofotasios, P. C., Al‐Qutayri, M., Damiani, E., & Debbah, M. A. (2022) · IEEE Wireless Communications · doi:10.1109/mwc.005.00334 · Cited by 48

Federated learning to comply with data protection regulations

Chalamala, S. R., Kummari, N. K., Singh, A. K., Saibewar, A., & Chalavadi, K. M. (2022) · CSI Transactions on ICT · doi:10.1007/s40012-022-00351-0 · Cited by 45

Data Privacy Threat Modelling for Autonomous Systems: A Survey From the GDPR's Perspective

Azam, N., Michala, A. L., Ansari, S., & Truong, N. B. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3227336 · Cited by 49

Misbehavior Detection in Vehicular Ad Hoc Networks Based on Privacy-Preserving Federated Learning and Blockchain

Lv, P., Xie, L., Xu, J. W., Wu, X., & Li, T. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3220779 · Cited by 43

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 46

A Survey and Guideline on Privacy Enhancing Technologies for Collaborative Machine Learning

Soykan, E. Ü., Karaçay, L., Karakoç, F., & Tomur, E. (2022) · IEEE Access · doi:10.1109/access.2022.3204037 · Cited by 45

Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach

Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41

Improving Global Generalization and Local Personalization for Federated Learning

Meng, L., Qi, Z., Wu, L., Du, X., Li, Z., Cui, L., & Meng, X. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3417452 · Cited by 41

FedKD-IDS: A robust intrusion detection system using knowledge distillation-based semi-supervised federated learning and anti-poisoning attack mechanism

Quyen, N. H., Duy, P. T., Nguyen, N. T., Khoa, N. H., & Pham, V.-H. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102807 · Cited by 40

Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis

Kazmi, S. H. A., Qamar, F., Hassan, R. B., Nisar, K., & Al‐Betar, M. A. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110358 · Cited by 40

Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches

Shah, S. T., Ali, Z., Waqar, M., & Kim, A. (2025) · Healthcare · doi:10.3390/healthcare13212760 · Cited by 41

Bandit-based data poisoning attack against federated learning for autonomous driving models

Wang, S., Li, Q., Cui, Z., Hou, J., & Huang, C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120295 · Cited by 38

Comparative analysis of open-source federated learning frameworks - a literature-based survey and review

Riedel, P., Schick, L., von Schwerin, R., Reichert, M., Schaudt, D., & Hafner, A. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02234-z · Cited by 38

Threats on Machine Learning Technique by Data Poisoning Attack: A Survey

Ahmed, I. M., & Kashmoola, M. Y. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-8059-5_36 · Cited by 38

Federated learning vulnerabilities, threats and defenses: A systematic review and future directions

Almutairi, S., & Barnawi, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.100947 · Cited by 35

Threats to federated learning A survey

Lyu, L., Yu, H., & Yang, Q. (2020) · arXiv · doi:10.48550/arxiv.2003.02133 · Cited by 221

Privacy and Robustness in Federated Learning: Attacks and Defenses

Lyu, L., Yu, H., Ma, X., Chen, C., Sun, L., Zhao, J., Yang, Q., & Yu, P. S. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3216981 · Cited by 548

A survey on security and privacy threats to federated learning

Zhang, J., Li, M., Zeng, S., Xie, B., & Zhao, D. (2021) · International Conference on Networking and Network Applications (NaNA) · doi:10.1109/nana53684.2021.00062 · Cited by 34

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

Data poisoning and adversarial attacks in federated learning

Jeelan, P. M., & Khan, K. N. (2026) · CRC Press eBooks · doi:10.1201/9781003660330-6