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

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Diversity Adversarial Training against Adversarial Attack on Deep Neural Networks

Kwon, H., & Lee, J. (2021) · Symmetry · doi:10.3390/sym13030428 · Cited by 18 · see citing works

From Gradient Leakage To Adversarial Attacks In Federated Learning

Lim, J. Q., & Chan, C. S. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506589 · Cited by 17 · see citing works

Evasion Attacks against Banking Fraud Detection Systems

Carminati, M., Santini, L., Polino, M., & Zanero, S. (2020) · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · Cited by 16 · see citing works

Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints

Mohammad, U., Sorour, S., & Hefeida, M. (2020) · 2020 IEEE International Conference on Communications Workshops (ICC Workshops) · doi:10.1109/iccworkshops49005.2020.9145450 · Cited by 16 · see citing works

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Machine Learning and Intelligent Communications

(2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73447-7 · Cited by 9 · see citing works

A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning

Zhao, J., Wu, X. G., Zhang, Y., Wu, Y., & Wang, Z. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86340-1_29 · Cited by 6 · see citing works

The Hidden Vulnerability of Distributed Learning in Byzantium

Mhamdi, E. M. E., Guerraoui, R., & Rouault, S. (2018) · arXiv · doi:10.48550/arxiv.1802.07927

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 · see citing works

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

Uddin, M. P., Xiang, Y., Hasan, M., Bai, J., Zhao, Y., & Gao, L. (2025) · ACM Computing Surveys · doi:10.1145/3727643 · Cited by 94 · see citing works

A Survey of Trustworthy Federated Learning: Issues, Solutions, and Challenges

Zhang, Y., Zeng, D., Luo, J., Fu, X., Chen, G., Xu, Z., & King, I. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3678181 · Cited by 90 · see citing works

A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study

Almutairi, S., & Barnawi, A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103295 · Cited by 19 · see citing works

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review

Guo, D., & Choo, K. R. (2025) · Journal of Medical Internet Research · doi:10.2196/68291 · Cited by 13 · see citing works

PPFLQB: A Privacy-Preserving Federated Learning enhanced Quantum-secure Blockchain layered framework

Sezer, B. B., & Turkmen, H. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101719 · Cited by 9 · see citing works

A survey of joint security-safety for function, information and human in industry 5.0

Hong, Y., Wu, J., & Guan, X. (2024) · Security and Safety · doi:10.1051/sands/2024014 · Cited by 11 · see citing works

PPFL: Privacy-Preserving Federated Learning Based on Differential Privacy and Personalized Data Transformation

Han, J., Wang, L., Liu, Z., Qin, B., Zhang, K., & Li, W. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3595533 · Cited by 8 · see citing works

AI and ML empowering 5G and shaping the 6G future: Models, metrics, architectures, and applications

Tulaskar, D. P., Sindhu, B., Chakole, N., Parteki, R., Leema, A. A., Balakrishnan, P. A., Avthanka, A., Girhe, R., Kulkarni, M. B., & Bhaiyya, M. L. (2025) · ICT Express · doi:10.1016/j.icte.2025.11.024 · Cited by 8 · see citing works

TrustFed: A Reliable Federated Learning Framework With Malicious-Attack Resistance

Hang, S., Zhou, J., Feng, G., & Niu, X. (2024) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2024.3510567 · Cited by 6 · see citing works

FL-SMPC++: A robust framework for privacy-preserving federated learning

Dib, O., Li, S., Li, Z., Abdallah, R., & Diallo, E.-H. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107380 · Cited by 5 · see citing works

Decentralized federated learning using validation loss for model sharing in crop disease classification

Kabala, D. M., Hafiane, A., Bobelin, L., & Canals, R. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103205 · Cited by 4 · see citing works

Detection of Existing Practices and Forecasting of Future Research in the Field of Cloud Technologies in Education

Yurchenko, A. O., Shamonia, V., Буртовий, Р., Bohoslavskyi, S., & Семеніхіна, О. В. (2024) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro60963.2024.10569612 · Cited by 4 · see citing works

Exploring the Limitations of Federated Learning: A Novel Wasserstein Metric-Based Poisoning Attack on Traffic Sign Classification

Almutairi, S., & Barnawi, A. (2025) · IEEE Access · doi:10.1109/access.2025.3584948 · Cited by 4 · see citing works

A robust and verifiable federated learning framework for preventing data poisonous threats in e-health

Alruwaili, E., & Moulahi, T. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1762346 · Cited by 3 · see citing works

From privacy to trust in the agentic era: a taxonomy of challenges in trustworthy federated learning through the lens of trust report 2.0

Rodríguez-Barroso, N., García-Márquez, M., Luzón, M. V., & Herrera, F. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104236 · Cited by 5 · see citing works

Federated Learning for Secure and Resilient AI Systems

Farooqi, S. A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2200-1.ch010 · Cited by 1 · see citing works

Resilient Federated Learning for DDoS Detection with Multi-Krum Aggregation and Anomaly Detection

Saha, S., Sayed, M. I., Rahman, M. M., Faezipour, M., & Bhatt, S. (2025) · International Conference on Smart Applications, Communications and Networking (SmartNets) · doi:10.1109/smartnets65254.2025.11106859 · Cited by 1 · see citing works

Integrating Identity-Based Identification Against Adaptive Adversaries in Federated Learning

Szelag, J. K., Chin, J., Ansell, L., & Yip, S.-C. (2025) · IEEE Access · doi:10.1109/access.2025.3609448 · Cited by 1 · see citing works

Defending federated learning against adversarial attacks: A systematic literature review

Babar, F. F., Khan, S., & Parkinson, S. (2026) · Cyber Security and Applications · doi:10.1016/j.csa.2026.100127 · Cited by 1 · see citing works

SECTION 28. TRENDS IN THE USE OF INFORMATION TECHNOLOGIES IN PROFESSIONAL TRAINING: ANALYSIS OF THE USE OF CLOUD TECHNOLOGIES IN EDUCATION

Yurchenko, A. O., & Момот, Р. (2024) · doi:10.30525/978-9934-26-405-4-28

A Review of Privacy-Preserving Decentralized Learning

Gitanshu, & Kumar, N. B. M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_29

Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: A Scoping Review (Preprint)Preprint

Guo, D., & Choo, K. R. (2024) · doi:10.2196/preprints.68291

Security Threats and Defence Mechanisms in Federated Learning: A Comprehensive Review

Ocran, A., & Effah, J. (2025) · doi:10.38124/ijisrt/25may617

Hidden Threats to Federated Learning: A Comprehensive Survey of Attack Paradigms, Defense Strategies, and Research DirectionsPreprint

su, Q., Zhang, G., li, H., & Ge, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5506730

A Federated Learning Framework for Secure Crop Disease Detection in Decentralized Agricultural Environments

Nasar, M., Kausar, M. A., & Nayyer, M. A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-12990-1_18

Data Poisoning Attacks in Federated Learning and Mitigating Label-Flipping Attacks With MSCDFL

Maheshwari, R., Nithya, T., Kokilavani, T., Keerthana, N. V., Sathyasutha, P., Geetha, S., & Amruthavarshini, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6224-3.ch005

Mapping the literature on factors influencing cybersecurity in the metaverse: A bibliometric analysis

Oliet-Villalba, J. M., Medina, J., Ferrer-Oliva, M., & Martínez-Herráiz, J.-J. (2026) · Computers & Security · doi:10.1016/j.cose.2026.104888

Privacy-Enhancing Federated Learning-Based IDS for IoT Networks Using Post-Quantum Secure Channels and Verifiable Secret Sharing

Syne, L., Hernández‐Goya, C., & Caballero‐Gil, P. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19770-2_16

Flower Gone? Exploring Insider Attacks and the Effects on Federated Learning

Copeland, A. M., & Meng, W. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-21177-4_5

FedVAE-KD: A Privacy-Preserving Federated Learning Framework for Scalable Wireless Network Intrusion Detection

Yagiz, M. A., Benamara, A., Deniz, Z., & Göktaş, P. (2026) · International Balkan Conference on Communications and Networking (BalkanCom) · doi:10.1109/balkancom71095.2026.11605017

Dual-side Control for Coordinated Attack Detection in Federated Learning

Hamdani, H., Benmohamed, E., & Ltifi, H. (2026) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit70676.2026.11631189

FL-Forensics: A Blockchain-Enabled Framework for Forensic Accountability and Auditability in Federated Learning

Mohammed, S., & Ngom, A. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-37274-1_26

A Critical Evaluation of Privacy and Security Threats in Federated Learning

Asad, M., Moustafa, A., & Yu, C. (2020) · Sensors · doi:10.3390/s20247182 · Cited by 51 · see citing works

Federated Learning with Internet of Things for Data Privacy and Security

Cherukuvada, S., Krishnaraj, N., & Bellam, K. (2023) · International Conference on Networking and Communications (ICNWC) · doi:10.1109/icnwc57852.2023.10127281 · Cited by 2 · see citing works

Security Model and Algorithms in Federate learning under IoT Critical InfrastructurePreprint

Jalali, N. A., & Chen, H. (2022) · Research Square · doi:10.21203/rs.3.rs-906546/v1

Federated Learning for IoT Applications, Attacks and Defense Methods

Qu, Z., Duan, R., Liu, Y., & Lu, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-42637-7_9

Vulnerabilities in federated learning

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