The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks
Abstract
Although federated learning offers a level of privacy by aggregating user data without direct access, it remains inherently vulnerable to various attacks, including poisoning attacks where malicious actors submit gradients that reduce model accuracy. In addressing model poisoning attacks, existing defense strategies primarily concentrate on detecting suspicious local gradients over plaintext. However, detecting non-independent and identically distributed encrypted gradients poses significant challenges for existing methods. Moreover, tackling computational complexity and communication overhead becomes crucial in privacy-preserving federated learning, particularly in the context of encrypted gradients. To address these concerns, we propose a robust privacy-preserving federated learning model resilient against model poisoning attacks without sacrificing accuracy. Our approach introduces an internal auditor that evaluates encrypted gradient similarity and distribution to differentiate between benign and malicious gradients, employing a Gaussian Mixture Model and Mahalanobis Distance for byzantine-tolerant aggregation. The proposed model utilizes Additive Homomorphic Encryption to ensure confidentiality while minimizing computational and communication overhead. Our model demonstrates superior performance in accuracy and privacy compared to existing strategies and encryption techniques, such as Fully Homomorphic Encryption and Two-Trapdoor Homomorphic Encryption. The proposed model effectively addresses the challenge of detecting maliciously encrypted non-independent and identically distributed gradients with low computational and communication overhead.
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In the citing paper
“Another promising technique is robust aggregation methods, where model updates are evaluated for anomalies before being integrated into the global model [72,73].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
In the citing paper
“Defense techniques such as adversarial training, where the model is pre-trained with adversarial samples, have displayed promise.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
In the citing paper
“In targeted poisoning attacks, malicious contributors corrupt the model to misidentify specific data, which could be utilized to target certain diseases or demographic groups.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
In the citing paper
“Designed to prevent malicious model updates from impacting the global model, this technique uses statistical approaches to filter irregularities in model updates, maintaining robustness even with a certain degree of compromised participants [72].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
In the citing paper
“Data manipulation attacks are crucial threats in FL, particularly in healthcare.”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
In the citing paper
“Untargeted poisoning, in contrast, aims to degrade model efficiency globally, risking patient results over the board [72,76].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [72]
Citations
Cites
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
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936
On the Convergence of FedAvg on Non-IID Data
Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2019) · arXiv · doi:10.48550/arxiv.1907.02189 · Cited by 1,575
FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Cao, X., Fang, M., Liu, J., & Gong, N. Z. (2021) · Proceedings 2021 Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24434 · Cited by 999
Data Poisoning Attacks Against Federated Learning Systems
Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927
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
Personalized Cross-Silo Federated Learning on Non-IID Data
Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., & Zhang, Y. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i9.16960 · Cited by 749
ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169918 · Cited by 371
Privacy-Enhanced Federated Learning Against Poisoning Adversaries
Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3108434 · Cited by 351 · cited 8×
“Model poisoning adversaries manipulate user input by submitting poisonous updates to local models to create arbitrary deviance from correct trends [3].”
BackgroundMethodInfluentialPrivacy-Preserving Collaborative Deep Learning With Unreliable Participants
Zhao, L., Wang, Q., Zou, Q., Zhang, Y., & Chen, Y. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2939713 · Cited by 215
Research works printed but not linked: 12
- Bhagoji, Analyzing federated learning through an adversarial lens, 634
- Fung, The limitations of federated learning in Sybil settings, 301
- Fang, Local model poisoning attacks to Byzantine-robust federated learning, 1623
- Mozaffari, Every vote counts: Ranking-based training of federated learning to resist poisoning attacks, 1721
- Blanchard, Machine learning with adversaries: Byzantine tolerant gradient descent, 30, 118
- El Mhamdi, The hidden vulnerability of distributed learning in Byzantium, 3521
- Xie, DBA: Distributed backdoor attacks against federated learning
- Bell, ACORN: Input validation for secure aggregation, 4805
- Shen, AUROR: Defending against poisoning attacks in collaborative deep learning systems, 508
- 2023, Availability With Redundancy
- Goodfellow, 2016, Deep Learning
- 2016, Paillier Cryptosystem
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“Destabilizing the model undermines the model’s generalization ability as well as its capability to detect zero-day attacks accurately [193, 194].”
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“As an emerging distributed machine learning paradigm, various related aspects of FL have been advanced in the fields of model aggregation [43], [44], [45], applications [46], [47], [48], privacy and security [49], [50], [51], and more.”
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“Recent works [16], [10], [28], [11] have revealed vulnerabilities in automotive communication systems, especially the CAN bus.”
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Eren, H., Karaduman, Ö., & Gençoğlu, M. T. (2025) · Applied Sciences · doi:10.3390/app15158704 · Cited by 28
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Kumar, A., Maulik, A., & Chinmaya, K. A. (2025) · IETE Technical Review · doi:10.1080/02564602.2025.2522083 · Cited by 21
“Security is another critical issue, as FEDL is susceptible to adversarial threats such as model poisoning and inference leakage [190].”
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“Several studies have endeavored to tackle this issue [21], [30], [31], [32], [33], [34], [35], [36], [47], [48], [71].”
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Liu, H., Guo, C., Ma, Y., & Wang, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.127409 · Cited by 10
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Yan, C., Lu, X., Lió, P., & Hui, P. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3576008 · Cited by 10
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“However, that does not seriously affect performance [20].”
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Jabbar, A., Huang, J., Jabbar, M. K., & Mahmood, T. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107203 · Cited by 9
Kumar, B. V. R., Gupta, S., Dwivedi, R., Alsekait, D. M., AbdElminaam, D. S., & Kilickaya, O. (2026) · Scientific Reports · doi:10.1038/s41598-026-37802-6 · Cited by 9
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Ren, Y., Yang, Z., Feng, G., & Zhang, X. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3540420 · Cited by 7
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“The primary objective of targeted attacks is to learn an adversarial perturbation δ for an input image x with the aim of misleading deep models into predicting a specific target class t .”
Decentralized IoMT Architecture for Privacy-Preserving Remote Patient Monitoring
Yin, L., Du, X., Cheng, Y., Li, J., Tong, N., & Li, F. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3575941 · Cited by 6
“Ryu et al. [29], Lin et al. [30], and Yazdinejad et al. [31] use multiparty collaboration and secure multiparty computation (MPC) to ensure model privacy.”
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
Kumar, G., Elicherla, S. L. R., Saxena, S., Swamy, K. A., Ashwini, P., & Kumar, U. P. (2025) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2025.101057 · Cited by 6
Mao, J., Wei, Z., Li, B., Zhang, R., & Song, L. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3602226 · Cited by 6
“For instance, to defend against model poisoning attacks, Ma et al. [18] proposed ShieldFL, a defense using homomorphic encryption to identify malicious encrypted gradients, while Yazdinejad et al. [19] developed a model with an internal auditor using statistical methods for the same purpose.”
Privacy-by-design AIoT vision for intelligent urban environments
Ivanovska, M., Kreft, J., Štruc, V., & Perš, J. (2025) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2025.103586 · Cited by 6
Liu, X., Sun, H., Sun, N., Jia, B., & Xiao, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131931 · Cited by 6
Verma, R., & Jailia, M. (2025) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-025-00797-y · Cited by 6
“Federated learning systems are highly susceptible to poisoning attacks where adversarial clients submit manipulated model updates to degrade global performance [42].”
Blockchain-enabled identity management for IoT: a multi-layered defense against adversarial AI
Usama, M., Aziz, A., Alasbali, N. A., Alturki, N. M., Hanif, M. U., & Rehman, M. U. (2026) · Scientific Reports · doi:10.1038/s41598-026-35208-y · Cited by 6
PriFFT: Privacy-Preserving Federated Fine-Tuning of Large Language Models via Hybrid Secret Sharing
You, Z., Dong, X., Cheng, K., Mu, X., Fu, J., Ma, S., Qu, Q., & Shen, Y. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3661572 · Cited by 6
“Abbas et al. present an HE-based privacy-preserving FL against model poisoning attacks with an internal auditor and Byzantine-tolerant gradient aggregation [23].”
Semantic-Aware Federated Blockage Prediction (SFBP) in Vision-Aided Next-Generation Wireless Network
Khan, A. R., Manzoor, H. U., Rais, R. N. B., Hussain, S., Mohjazi, L., Imran, M. A., & Zoha, A. (2025) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2024.3525338 · Cited by 6
“Recent studies have explored these challenges in different applications, highlighting the need for robust aggregation mechanisms and adaptive strategies to ensure reliable performance [16], [17].”
Alamer, A. M. A., & Basudan, S. (2025) · IEEE Access · doi:10.1109/access.2025.3545786 · Cited by 6
“Schemes such as RPPFL in [39] face difficulties in distinguishing the local trained model on non-IID data, primarily due to the inherent challenge of differentiating between poisonous and benign gradients in the presence of divergence.”
InfluentialA Verifiable, Privacy-Preserving, and Poisoning Attack-Resilient Federated Learning Framework
Mbonu, W. E., Maple, C. R., Epiphaniou, G., & Panchev, C. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9040085 · Cited by 7
Verma, P., O’Shea, D., Newe, T., Mehta, N., Bharot, N., & Breslin, J. G. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07292-w · Cited by 6
Kokate, S., & Shrawankar, U. N. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70139 · Cited by 7
“A strong privacy-preserving FL (PPFL) model that is resistant to model poisoning assaults without compromising accuracy was suggested by Yazdinejad et al. [34] in order to alleviate the issues.”
Agarwal, U., Rishiwal, V., Yadav, M., & Yadav, P. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01994-x · Cited by 5
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders
Li, K., Hu, S., Wu, B., Zou, S., Ni, W., & Dressler, F. (2025) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc65282.2025.11059509 · Cited by 5
“The malicious device is either a manipulated benign IoT device or an attacker, aiming to minimize the training accuracy of federated learning [19].”
Attentive neural processes based on reliable inferences for industrial equipment anomaly detection
Huang, Y., Yang, B. Y., Wang, S., Xie, K., Wang, Y., Yi, L., & Dong, N. (2025) · Computers in Industry · doi:10.1016/j.compind.2025.104331 · Cited by 5
Benfriha, S., Labraoui, N., Salameh, H. B., & Bensaid, R. (2025) · Cluster Computing · doi:10.1007/s10586-025-05188-1 · Cited by 5
“Yazdinejad et al. [28] presented a robust privacy-preserving FDL model designed to resist model poisoning attacks without compromising accuracy.”
Jin, C., Wang, J., Lu, W. D., Luo, C., Liu, J., Chen, G., & Zhai, Z. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00256-3 · Cited by 5
“To further enhance privacy, Yazdinejad et al. (2024) incorporated differential privacy mechanisms to secure model parameters on edge devices in beyond-5G networks.”
Wu, J., Luo, F., Sun, T., Wang, H., & Zhang, W. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3617070 · Cited by 7
“To improve scalability, more recent works [30], [31], [32] adopt single-key CKKS [39], where all users encrypt their data under a shared key.”
InfluentialSumayli, M., & Anubi, O. M. (2026) · IEEE Access · doi:10.1109/access.2026.3653933 · Cited by 5
“This decentralized approach significantly reduces privacy risks, mitigates communication overhead, and improves system robustness against data breaches [20].”
Sharma, N. A., Kumar, K. N. P., Khorshed, M. T., Ali, A. B. M. S., Khalid, H. M., Muyeen, S. M., & Jose, L. (2024) · Information · doi:10.3390/info15090558 · Cited by 5
SensFL: Privacy-Preserving Vertical Federated Learning with Sensitive Regularization
Cai, B., Zhang, C., Liu, Z., Xu, X., Hu, F., Dai, J., & Wang, W. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.055596 · Cited by 5
VIFL: vulnerability identification using federated learning in the internet of things systems
Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., Tari, Z., & Zomaya, A. Y. (2024) · Computing · doi:10.1007/s00607-024-01365-z · Cited by 5
“However, FL-based vulnerability identification still faces three key challenges [9–11]: data heterogeneity (statistical heterogeneity), system heterogeneity, and ensuring the reasonable level of privacy guarantee when the FL used to train ML models on data containing sensitive informationn.”
A Model of Encrypted Network Traffic Classification that Trades Off Accuracy and Efficiency
Yu, L., Yuan, J., Zheng, J., & Yang, N. (2024) · Journal of Network and Systems Management · doi:10.1007/s10922-024-09892-y · Cited by 6
“A strong privacy-preserving federated learning model that can withstand model poisoning attacks without compromising accuracy has been proposed by Yazdinejad et al. [67] to overcome these problems.”
Feature engineering based intelligent intrusion detection system for IoT network security
Sadhwani, S., Sriwastawa, A., Muthalagu, R., & Pawar, P. M. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02631-y · Cited by 5
“In recent years, access to computer systems has risen dramatically and intruders have found several ways to hide their identities with ease [15, 33].”
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
Privacy-Preserving Multilayer Community Detection via Federated Learning
Ma, S., Xu, X.-K., & Xiao, J. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3493967 · Cited by 5
“Yazdinejad et al. [42] proposed a framework that utilizes the Gaussian mixture model and marginal distance for Byzantine fault-tolerant aggregation to effectively deal with non-IID.”
Metadata
- Authors
- Yazdinejad, A., Dehghantanha, A., Karimipour, H., Srivastava, G., & Parizi, R. M.
- Affiliations
- University of Guelph (CA)
- University of Calgary (CA)
- Brandon University (CA)
- Kennesaw State University (US)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2024
- Volume
- 19
- Pages
- 6693-6708
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4400076000
- Cited by
- 380
- References
- 30
- Citations by year
- 2026: 139 · 2025: 205 · 2024: 33
- FWCI
- 55.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- privacy-preserving federated learning, model poisoning, additive homomorphic encryption, Gaussian mixture model, Mahalanobis distance, communication overhead
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