The DOI matches its Crossref record; 50 of the 53 references it cites were found in a registry or an index.
Threats to Federated Learning
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In the citing paper
“However, DRACO assumes that each participant can access other participants' data, limiting its practicability in FL.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [145]
In the citing paper
“DRACO is robust to arbitrarily malicious computing nodes, while being orders of magnitude faster than state-of-the-art robust distributed systems.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [145]
In the citing paper
“Unfortunately, median-based rules can incur a prohibitive computational overhead in large-scale settings [145].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [145]
In the citing paper
“Chen et al. [145] presented DRACO, a framework for robust distributed training via algorithmic redundancy.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [145]
Citations
Cites
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Poisoning Attacks against Support Vector Machines
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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
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Federated Learning of Deep Networks using Model AveragingPreprint
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Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001
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Self-citationMitigating Sybils in Federated Learning Poisoning
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Agarwal, N., Suresh, A. T., Yu, F., Kumar, S., & McMahan, H. B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10559 · Cited by 373
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
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Self-citationSupport Vector Machines Under Adversarial Label Noise
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Self-citationDistributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent
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Self-citationDRACO: Byzantine-resilient Distributed Training via Redundant GradientsPreprint
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Self-citationHow to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
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Self-citation
Research works printed but not linked: 4
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Cited by
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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
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
Self-citationAdvances, 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
Metadata
- 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
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 3-16
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- 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
- 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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