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AdversaGuard: A Distributed Data Poisoning Benchmark for Parallel AI

SessionPoster Presentations (Research, ACM SRC Grads/Undergrads)

DescriptionThis study introduces FoodSAFE, a novel high performance computing (HPC)-based distributed data poisoning (DDP) framework designed to benchmark adversarial resilience and training performance. The framework is tested across eight distinct configurations—seven distributed frameworks and one non-distributed baseline. FoodSAFE evaluates three diverse food-related datasets and four model architectures, ranging from small neural networks to large-scale transformers. The framework integrates eight advanced adversarial attacks: FGSM, PGD, DeepFool, One-Pixel, Universal, Carlini-Wagner, Trojan, and Boundary. It investigates how data, model, and hybrid parallelization strategies affect scalability, memory constraints, and vulnerability under real-world conditions. Additionally, the study presents the AdversaGuard app to enable live testing of these DDP techniques. Results indicate that while some architectures show greater tolerance to adversarial poisoning, larger models often exhibit heightened vulnerability, highlighting the critical need for adaptive and scalable defense strategies in modern AI systems.

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