The adjective “federated” is widely used in computer science, yet with diverging meanings that often lead toconfusion, especially in interdisciplinary discussions, collaborative authorship or when building research consortia.Originally rooted in political theory, federation denotes cooperation between autonomous entities withoutloss of sovereignty. Recently, however, “federated” now also describes architectures ranging from distributedquerying and data integration to machine learning, research infrastructures, and collaborative knowledge or dataecosystems.Motivated by recurring ambiguity in discussions among co-authors, this poster tries to distinguish prominentuses of the adjective “federated”. (e.g. federated SPARQL querying, federated learning, federated data warehousing,national initiatives such as the Swiss Personalized Health Network (SPHN), and the Wikimedia ecosystem). Wereview where these concepts overlap, where they diverge, and whether they should be treated as distinct notions.By making implicit assumptions explicit, we try to bring some clarity and hopefully practical guidance onwhen and how the term “federated” should be used
Waagmeester et al. (Sun,) studied this question.