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Found 3 papers, 2 papers with code
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU).
LLM-Grounder: Open-Vocabulary 3D Visual Grounding with Large Language Model as an Agent
While existing approaches often rely on extensive labeled data or exhibit limitations in handling complex language queries, we propose LLM-Grounder, a novel zero-shot, open-vocabulary, Large Language Model (LLM)-based 3D visual grounding pipeline.
Characterizing Internal Evasion Attacks in Federated Learning
However, combining adversarial training with personalized federated learning frameworks increases relative internal attack robustness by 60% compared to federated adversarial training and performs well under limited system resources.
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