Social and real-world considerations such as robustness, fairness, social welfare, and multi-agent tradeoffs have given rise to multi-distribution learning paradigms. In recent years, these paradigms have been studied by several disconnected communities and under different names, including collaborative learning, distributional robustness, and fair federated learning. In this short tutorial, I will highlight the importance of multi-distribution learning paradigms in general, introduce technical tools for addressing them, and discuss how these problems relate to classical and modern consideration in data driven processes.