Machine learning models are often seen as black-box systems that output point predictions without indicating how confident they are in those predictions. Recently, Conformal Prediction (CP) has emerged as a powerful framework to address this issue by transforming point predictions into set-valued predictions with probabilistic guarantees. In this talk, I will introduce CP and briefly present some key challenges and recent advances in the area. I will first discuss how to perform CP in a Federated Learning setting, showing that a single round of communication is sufficient to match the […]