Distributed learning is a methodological approach within machine learning and data science focused on training models or performing computations across multiple decentralized data sources or computational nodes without requiring the central aggregation of raw data. This research area investigates algorithms, communication protocols, and system designs that enable collaborative model building and data analysis while preserving data locality and privacy, addressing key challenges such as communication efficiency, data heterogeneity, security, and scalability inherent in distributed computing environments.