Towards Case-based Interpretability for Medical Federated Learning

Even though federated learning's potential to overcome Case-based interpretability is vital in explaining medical some of the current AI flaws is currently widely recognized, Artificial Intelligence (AI) model decisions. Generating it also introduces new challenges. The decentralized nature explanations for AI model decisions is paramount to increasing of federated learning guarantees compliance with privacy trust and allowing widespread adoption in clinical regulations but, at the same time, inhibits data access and practice [1]. We can find several approaches to producing inspection [7]. Non-accessible data means that identifying explanations in the scientific literature, from saliency maps bugs or detecting biases is impossible following conventional (highlighting image pixels driving the decision) to textual approaches. The same is true for case-based explainability.