Threats to Federated Learning
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DRACO assumes that each participant can access other participants' data, limiting its practicability in FL.
“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]
DRACO is robust to arbitrarily malicious computing nodes while being orders of magnitude faster than state-of-the-art robust distributed systems.
“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]
Median-based rules can incur prohibitive computational overhead in large-scale settings.
“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]
Chen et al. presented DRACO, a framework for robust distributed training via algorithmic redundancy.
“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]
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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
- 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 › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, privacy preservation, poisoning attacks, inference attacks, threat model, data privacy, adversarial attacks, robustness
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