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This paper analyzes the transferability of adversarial attacks in federated learning systems and proposes a defense mechanism based on adversarial training to enhance model robustness.
PRA-RAG is a provably robust aggregation algorithm for Retrieval-Augmented Generation that defends against poisoning attacks on retrieved texts. It uses geometric structures in the embedding space to identify robust subsets and provides theoretical bounds on attack impact, reducing attack success rate to as low as 1% while maintaining accuracy.