BadGD: A unified data-centric framework to identify gradient descent
vulnerabilities
BadGD: A unified data-centric framework to identify gradient descent
vulnerabilities
We present BadGD, a unified theoretical framework that exposes the vulnerabilities of gradient descent algorithms through strategic backdoor attacks. Backdoor attacks involve embedding malicious triggers into a training dataset to disrupt the model's learning process. Our framework introduces three novel constructs: Max RiskWarp Trigger, Max GradWarp Trigger, and Max GradDistWarp …