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Learning to Detect Malicious Clients for Robust Federated Learning
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Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.
Forward citations
Cited by 5 Pith papers
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DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
DFedReweighting is a unified reweighting method for decentralized federated learning that customizes aggregation via target metrics and strategies to improve fairness, Byzantine robustness, and other objectives while ...
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FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks
FLAegis defends federated learning by SAX-transforming client updates, spectral-clustering them to filter malicious clients, and applying FFT-based robust aggregation, outperforming several baselines on FEMNIST.
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Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
The submission combines an attention prediction abstract with a federated learning body, so the claimed result cannot be evaluated from the provided material.
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BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning
BoBa uses data distribution inference and overlapping clustering with voting to detect backdoor attacks in non-IID federated learning, claiming attack success rates below 0.001.
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Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats
Sentinel signs a TEE-attested record of each client's control-flow and variable usage and admits only updates whose attestation passes, reaching ASR 0 for its modeled attacks.
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