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Aug 17, 2022 · In this work, we consider attack models ranging from strong ones: q omniscient adversaries with full knowledge of the defense protocol that can ...
However, abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Such behavior can be attributed to ...
Jul 9, 2024 · Our algorithms rely on redundant task assignments coupled with detection of adversarial behavior. We also show the convergence of our method to ...
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Posted: 2 days ago
Posted: 2 days ago
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What are defenses against Byzantine attacks in distributed deep neural networks?
What are Byzantine attacks?
Vaswani, and A. Ramamoorthy, “Detection and Mitigation of Byzantine Attacks in Distributed Training,” IEEE/ACM Transactions on Networking (ToN), October 2023.
Mar 7, 2024 · Experimental results show that our BCML can tolerate Byzantine attacks without compromising convergence accuracy with lower time consumption, ...
RFVIR employs the Median Absolute Deviation (MAD) method to detect anomalies caused by Byzantine attackers. Besides, RFVIR also effectively combines clipping to ...
Missing: Mitigation | Show results with:Mitigation
PDF | Prior solutions for mitigating Byzantine failures in federated learning, such as element-wise median of the stochastic gradient descent (SGD).
We study the problem of designing optimal distributed detection parameters in a tree network in the presence of Byzantines.
Oct 30, 2024 · Our approach is assessed using a tailored neural network model applied to the MNIST dataset, achieving 97% accuracy in detecting Byzantine ...
Missing: Mitigation | Show results with:Mitigation