THE PRIVACY-EXPLAINABILITY TRADE-OFF: UNRAVELING THE IMPACTS OF DIFFERENTIAL PRIVACY AND FEDERATED LEARNING ON ATTRIBUTION METHODS

The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods

Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations.However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards.This is underscored by the on

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2-(4-Fluorobenzylidene)propanedinitrile: monoclinic polymorph

click here The title compound, C10H5FN2, is a monoclinic (P21/c) polymorph of the previously reported triclinic (P-1) form [Antipin et al.(2003).J.Mol.Struct.650, 1–20].The 13 non-H atoms in the title polymorph are almost coplanar (r.m.s.deviation = 0.020 Å); a small twist between the fluorobenzene and dinitrile groups [C—C&#

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