Today, access to high-quality data has become the key bottleneck to deploying Machine Learning (ML). Often, data that is most valuable is locked away in inaccessible silos due to unfavorable incentives and ethical-legal restrictions. This is starkly evident in healthcare, where such barriers have led to highly biased and underperforming tools.

In my talk, we will dive into my collaborations with public health organizations facing such issues, and see how collaborative systems (such as federated learning) prove a natural solution. Collaborative learning can remove barriers to data sharing by respecting the privacy and interests of the data providers. Yet, for these systems to truly succeed, we must confront three fundamental challenges. These systems need to i) be efficient and scale to large networks, ii) provide reliable and trustworthy training and predictions, and iii) manage the divergent goals and interests of the participants. We discuss how tools from optimization, statistics, and economics can be leveraged to address these challenges.