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