Many modern machine learning paradigms require large amounts of data and computation power that is rarely seen in one place or owned by one agent. In recent years, methods such as federated learning have been embraced as an approach for bringing about collaboration across learning agents. In practice, the success of these methods relies upon our ability to pool together the efforts of large numbers of individual learning agents, data set owners, and curators. In this talk, I will discuss how recruiting, serving, and retaining these agents requires us to address agents’ needs, limitations, and responsibilities. In particular, I will discuss two major questions in this field. First, how can we design collaborative learning mechanisms that benefit agents with heterogeneous learning objectives? Second, how can we ensure that the burden of data collection and learning is shared equitably between agents?