RobustFed: A Truth Inference Approach for Robust Federated Learning
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Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to collaboratively train a global model under a central server's orchestration while keeping local data private. However, the aggregation step in federated learning is vulnerable to adversarial attacks as the central server cannot enforce clients' behavior. As a result, the performance of the global model and convergence of the training process can be affected under such attacks. To mitigate this vulnerability, existing works have proposed robust aggregation methods such as median based aggregation instead of averaging. While they ensure some robustness against Byzantine attacks, they are still vulnerable to label flipping and Gaussian noise attacks. In this paper, we propose a novel robust aggregation algorithm inspired by the truth inference methods in crowdsourcing by incorporating the clients' reliability into aggregation. We evaluate our solution on three real-world datasets with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning and is resilient to various types of attacks, including noisy data attacks, Byzantine attacks, and label flipping attacks.
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The ASR of label-flipping attacks can be increased when the attacker controls more compromised clients.
“Some studies have shown that the attack success rate (ASR) of label-flipping attacks can be increased when the attacker controls more compromised clients [233], [234].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [233]
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- Authors
- Tahmasebian, F., Lou, J., & Xiong, L.
- Affiliations
- Emory University (US)
- Xidian University (CN)
- Venue
- Proceedings of the 31st ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2022-10-17
- Pages
- 1868-1877
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3183640384
- Cited by
- 26
- References
- 39
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 5 · 2022: 2 · 2021: 1
- FWCI
- 3.04
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- CNS-1952192
- Mitsubishi Pharma America #BNI-ALS-005
- CNS-2125530
- National Institutes of Health (NIH) R01LM013712
- Cisco Research #2738379
- National Science Foundation (NSF) CNS-2124104
- 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 › Machine Learning and Data Classification
- Keywords
- robust federated learning, truth inference, robust aggregation, Byzantine attacks, label-flipping attacks, crowdsourcing, data privacy
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