11 papers
Performance and Cost-Aware Cache Provisioning
Ridwanul Tanvir, George Kesidis
While traditional cache policy evaluations fix capacity - often at 0.1% of the dataset - and measure the resulting hit rate, practical edge-cloud deployments require balancing both…
CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs
Zhengxing Li, David J. Miller, Guangmingmei Yang +1
While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such methods for LLMs. First, the LLM i…
A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization
Guangmingmei Yang, David J. Miller, George Kesidis
Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks and backdoors (Trojans). In…
Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization
Guangmingmei Yang, David J. Miller, George Kesidis
Most post-training backdoor detection methods rely on attacked models exhibiting extreme outlier detection statistics for the target class of an attack, compared to non-target clas…
GPU Cluster Scheduling for Network-Sensitive Deep Learning
Aakash Sharma, Vivek M. Bhasi, Sonali Singh +3
We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the…
Inverting Trojans in LLMs
Zhengxing Li, Guangmingmei Yang, Jayaram Raghuram +2
While effective backdoor detection and inversion schemes have been developed for AIs used e.g. for images, there are challenges in "porting" these methods to LLMs. First, the LLM i…