APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning
Abstract
Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the global model’s availability. Currently, most existing works rely on an Independently Identical Distribution (IID) situation and identify malicious gradients using plaintext. However, we demonstrates that current works cannot handle the data heterogeneity scenario challenges and that publishing unencrypted gradients imposes significant privacy leakage problems. Therefore, we develop APFed, a layered privacy-preserving defense mechanism that significantly mitigates the effects of poisoning attacks in data heterogeneity scenarios. Specifically, we exploit HE as the underlying technique and employ the median coordinate as the benchmark. Subsequently, we propose a secure cosine similarity scheme to identify poisonous gradients, and we innovatively use clustering as part of the defense mechanism and develop a hierarchical aggregation that enhances our scheme’s robustness in IID and non-IID scenarios. Extensive evaluations on two benchmark datasets demonstrate that APFed outperforms existing defense strategies while reducing the communication overhead by replacing the expensive remote communication method with inexpensive intra-cluster communication.
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Poisoning attack aims to use poisoned data to disrupt the model training and reduce the federated model accuracy.
“Poisoning attack: In the FL system, a poisoning attack (Bhagoji et al. 2019;Chen et al. 2023) aims to use poisoned data to disrupt the model training and reduce the federated model accuracy.”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Chen et al. 2023
Cites
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 · see citing works
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., & Levine, S. (2017) · arXiv · doi:10.48550/arxiv.1703.03400 · Cited by 8,512 · see citing works
Public-Key Cryptosystems Based on Composite Degree Residuosity Classes
Paillier, P. (2007) · Lecture Notes in Computer Science · doi:10.1007/3-540-48910-x_16 · Cited by 7,250 · see citing works
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 · see citing works
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023 · see citing works
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077 · see citing works
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S. J., Stich, S. U., & Suresh, A. T. (2019) · arXiv · doi:10.48550/arxiv.1910.06378 · Cited by 1,734 · see citing works
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495 · see citing works
(2017) · neural information processing systems · Cited by 1,413 · see citing works
Sattler, F., Muller, K.-R., & Samek, W. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015958 · Cited by 1,355 · see citing works
Federated Learning for Mobile Keyboard Prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., & Ramage, D. (2018) · arXiv · doi:10.48550/arxiv.1811.03604 · Cited by 1,148 · see citing works
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Yin, D., Chen, Y., Ramchandran, K., & Bartlett, P. (2018) · arXiv · doi:10.48550/arxiv.1803.01498 · Cited by 1,130 · see citing works
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 · see citing works
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 · see citing works
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Fang, M., Cao, X., Jia, J., & Gong, N. Z. (2019) · arXiv · doi:10.48550/arxiv.1911.11815 · Cited by 828 · see citing works
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural NetworksPreprint
Shafahi, A., Huang, W., Najibi, M., Suciu, O., Studer, C., Dumitraş, T., & Goldstein, T. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000460348 · Cited by 626 · see citing works
DBA: Distributed Backdoor Attacks against Federated Learning
Xie, C., Huang, K., Chen, P., & Li, B. (2020) · International Conference on Learning Representations · Cited by 468 · see citing works
Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-Y., Lee, K., & Papailiopoulos, D. (2020) · arXiv · doi:10.48550/arxiv.2007.05084 · Cited by 406 · see citing works
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 · see citing works
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 · see citing works
Can You Really Backdoor Federated Learning?
Sun, Z., Kairouz, P., Suresh, A. T., & McMahan, H. B. (2019) · arXiv · doi:10.48550/arxiv.1911.07963 · Cited by 348 · see citing works
(2019) · neural information processing systems · Cited by 318 · see citing works
The Limitations of Federated Learning in Sybil Settings
Fung, C., Yoon, C. J. M., & Beschastnikh, I. (2020) · Recent Advances in Intrusion Detection · Cited by 265 · see citing works
Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
Chen, Y., Su, L., & Xu, J. (2018) · Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems · doi:10.1145/3219617.3219655 · Cited by 228 · see citing works
Robust Federated Learning in a Heterogeneous EnvironmentPreprint
Ghosh, A., Hong, J., Yin, D., & Ramchandran, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06629 · Cited by 204 · see citing works
On the Byzantine Robustness of Clustered Federated Learning
Sattler, F., Müller, K., Wiegand, T., & Samek, W. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054676 · Cited by 184 · see citing works
A Little Is Enough: Circumventing Defenses For Distributed LearningPreprint
Baruch, M., Baruch, G., & Goldberg, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06156 · Cited by 184 · see citing works
Efficient Public-Key Cryptosystems Provably Secure Against Active Adversaries
Paillier, P., & Pointcheval, D. (1999) · Lecture notes in computer science · doi:10.1007/978-3-540-48000-6_14 · Cited by 159 · see citing works
Provably Secure Federated Learning against Malicious Clients
Cao, X., Jia, J., & Gong, N. Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i8.16849 · Cited by 141 · see citing works
PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization
Liu, B., Guo, Y., & Chen, X. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449847 · Cited by 124 · see citing works
Self-citationAn Incentive Mechanism Design for Efficient Edge Learning by Deep Reinforcement Learning Approach
Zhan, Y., & Zhang, J. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155268 · Cited by 107 · see citing works
Enabling Execution Assurance of Federated Learning at Untrusted Participants
Zhang, X., Li, F., Zhang, Z., Li, Q. L., Wang, C., & Wu, J. (2020) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom41043.2020.9155414 · Cited by 93 · see citing works
Continual Local Training For Better Initialization Of Federated Models
Yao, X., & Sun, L. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9190968 · Cited by 88 · see citing works
Gaff, B. M., Sussman, H. E., & Geetter, J. S. (2014) · Computer · doi:10.1109/mc.2014.161 · Cited by 56 · see citing works
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Xie, P., Wu, B., & Sun, G. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/671 · Cited by 32 · see citing works
Federated AI for building AI Solutions across Multiple AgenciesPreprint
Verma, D., Julier, S., & Cirincione, G. (2018) · UCL Discovery (University College London) · Cited by 24 · see citing works
Universal Multi-Party Poisoning AttacksPreprint
Mahloujifar, S., Mahmoody, M., & Mohammed, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03474 · Cited by 9 · 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
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Wang, X., Wang, B., Wu, Y., Ning, Z., Guo, S., & Yu, F. R. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3446585 · Cited by 93 · see citing works
Li, K., Li, C., Yuan, X., Li, S., Zou, S., Ahmed, S. S., Ni, W., Niyato, D. T., Jamalipour, A., Dressler, F., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3603957 · Cited by 61 · see citing works
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- Authors
- Chen, X., Yu, H., Jia, X., & Yu, X.
- Affiliations
- Harbin Institute of Technology (CN)
- City University of Hong Kong (HK)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2023
- Volume
- 18
- Pages
- 5749-5761
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4386702599
- Cited by
- 50
- References
- 38
- Citations by year
- 2026: 20 · 2025: 18 · 2024: 11 · 2023: 1
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- National Natural Science Foundation of China (62172123, 62302122)
- Natural Science Foundation of Heilongjiang Province of China (YQ2021F007)
- 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 › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, poisoning attacks, privacy-preserving defense, data heterogeneity, non-IID data, homomorphic encryption, clustering, hierarchical aggregation, communication cost reduction
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