With the never-ending revolutions of data-driven approaches that started already more than a decade ago, it is surprising at first that the pace of ML discoveries in medicine seems substantially slower than the ones found in consumer applications such as ChatGPT. The answer to that paradox is relatively simple: in healthcare, data is hard to access as it is expensive and siloed in medical institutions and thus data-hungry methods trained on only one center on limited data often fail to generalize to another. In order to break those silos while protecting patient data and thus enabling new medical discoveries via ML, federated learning (FL) is a promising approach. However, in practice, applying FL in real-life contexts presents numerous challenges. This talk will use some of the FL research projects that Owkin has spearheaded in order to illustrate the realities of FL in healthcare and discuss the remaining milestones on the road to FL technologies and ML becoming the new engine of medicine research.