Cited by 448Privacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningCryptography and Data Security

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Threats to Federated Learning

Lyu, Lingjuan, Yu, Han, Zhao, Jun, Yang, Qiang·2020·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-63076-8_1

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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
Self-citation
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
Self-citation
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

Show all 53 references

Research works printed but not linked: 4

Li, D., Wang, J.: FedMD: heterogenous federated learning via model distillation. arXiv preprint arXiv:1910.03581 (2019) · arXiv
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282 (2017)
Pan, X., Zhang, M., Wu, D., Xiao, Q., Ji, S., Yang, M.: Justinian’s GAAvernor: robust distributed learning with gradient aggregation agent. In: USENIX Security Symposium (2020)
Phong, L.T., Aono, Y., Hayashi, T., Wang, L., Moriai, S.: Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans. Inf. Forensics Secur. 13(5), 1333–1345 (2018) · DOI · Paper page

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Authors: Lyu, L., Yu, H., Zhao, J., & Yang, Q.
Affiliations: National University of Singapore (SG)
Nanyang Technological University (SG)
Hong Kong University of Science and Technology (HK)
Corresponding author: Lyu, Lingjuan
ORCID: Lyu, Lingjuan
Yu, Han
Zhao, Jun
Yang, Qiang
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2020
Pages: 3-16
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-63076-8_1
OpenAlex: W3109695251
Semantic Scholar: f265e83d8b323d89369cb8a9114af4dfa36a7db1
Cited by: 444
References: 53
Citations by year: 2026: 65 · 2025: 88 · 2024: 118 · 2023: 77 · 2022: 64 · 2021: 28 · 2020: 4
FWCI: 16.52
Type: conference paper
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
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: federated learning, privacy preservation, poisoning attacks, inference attacks, threat model, data privacy, adversarial attacks, robustness

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