Detecting Malicious Model Updates from Federated Learning on Conditional Variational Autoencoder
Abstract
In federated learning, the central server combines local model updates from the clients in the network to create an aggregated model. To protect clients' privacy, the server is designed to have no visibility into how these updates are generated. The nature of federated learning makes detecting and defending against malicious model updates a challenging task. Unlike existing works that struggle to defend against Byzantine clients, the paper considers defending against targeted model poisoning attack in the federated learning setting. The adversary aims to reduce the model performance on targeted subtasks while maintaining the main task's performance. This paper proposes Fedcvae, a robust and unsupervised federated learning framework where the central server uses conditional variational autoencoder to detect and exclude malicious model updates. Since the reconstruction error of malicious updates is much larger than that of benign ones, it can be used as an anomaly score. We formulate a dynamic threshold of reconstruction error to differentiate malicious updates from normal ones based on this idea. Fedcvae is tested with extensive experiments on IID and non-IID federated benchmarks, showing a competitive performance over existing aggregation methods under Byzantine attack and targeted model poisoning attack.
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The PCA-based method requires prior knowledge of the targeted backdoor label, which is hard to know in practical defense implementation.
“The PCA-based method requires prior knowledge of the targeted backdoor label, which is also hard to know in practical defense implementation.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [203]
Low-dimensional defense methods are incompatible with the SecAgg protocol, and participant privacy in WFL cannot be guaranteed.
“The low-dimensional methods are also incompatible with the SecAgg protocol, and the privacy of the participants in the WFL network cannot be guaranteed.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [203]
The PCA-based defense identifies the biggest cluster as the benign update and malicious updates as lying in a visibly different cluster.
“The biggest cluster is recognized as the benign update, while the malicious updates lie within a visibly different cluster.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [203]
The low-dimensional defense methods can only distinguish label-flipping-based backdoor attacks and cannot deal with trigger-based backdoor attacks.
“Meanwhile, the above-mentioned methods can only distinguish label-flipping-based backdoor attacks and cannot deal with trigger-based backdoor attacks.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [203]
Several low-dimensional anomaly detection methods have been proposed.
“Instead of simply using the norm clipping defense method, several lowdimensional anomaly detection methods have been proposed [202], [203].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [203]
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- Authors
- Gu, Z., & Yang, Y.
- Affiliations
- National University of Defense Technology (CN)
- Venue
- 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
- Publisher
- IEEE
- Published
- 2021-05
- Pages
- 671-680
- ISSN
- 1530-2075 (ISSN-L)
- OpenAlex
- W3177173743
- Cited by
- 47
- References
- 41
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 10 · 2023: 19 · 2022: 7 · 2021: 1
- FWCI
- 4.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Parallel and Distributed Processing Symposium (IPDPS) (publishedVersion)
- 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 › Network Security and Intrusion Detection
- Keywords
- federated learning, model poisoning, conditional variational autoencoder, reconstruction error, anomaly detection, non-IID data, Byzantine attacks
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