Ozgur B. Akan

The paper proposes an Augmented Model maniPulation (AugMP) strategy, utilizing graph representation learning, to effectively and stealthily manipulate federated fine-tuning of LLMs, significantly degrading global model performance while evading standard defenses.

This paper formulates receiver selection for large-aperture wave receivers as a modal-recoverability problem and proposes a framework with three nested recoverability criteria for individual modal degrees, joint target subspace, and target modes.