Argus detects backdoors in decentralized learning by local trigger analysis and neighbor similarity checks on consistency, with theoretical convergence guarantees and empirical reductions in attack success up to 90 points.
Decentralized learning made easy with decentralizepy
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A systematic evaluation of GPU memory and utilization estimators across analytical, library-based, and ML paradigms identifies key limitations in generalization, integration overhead, and hardware variability for training-aware resource management.
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Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning
Argus detects backdoors in decentralized learning by local trigger analysis and neighbor similarity checks on consistency, with theoretical convergence guarantees and empirical reductions in attack success up to 90 points.
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GPU Memory and Utilization Estimation for Training-Aware Resource Management: Opportunities and Limitations
A systematic evaluation of GPU memory and utilization estimators across analytical, library-based, and ML paradigms identifies key limitations in generalization, integration overhead, and hardware variability for training-aware resource management.