Distributed machine learning is a field of research and a methodological approach focused on developing and applying machine learning algorithms across multiple interconnected computing nodes. It investigates techniques for partitioning data and computational tasks, managing communication and synchronization, and ensuring the scalability, efficiency, and convergence of learning processes in decentralized environments, thereby enabling the training of models on datasets exceeding the capacity of a single machine and facilitating collaborative or privacy-preserving learning paradigms.