To federate or not to federate, that is the query

The adjective “federated” is widely used in computer science, yet with diverging meanings that often lead to
confusion, especially in interdisciplinary discussions, collaborative authorship or when building research consortia.
Originally rooted in political theory, federation denotes cooperation between autonomous entities without
loss of sovereignty. Recently, however, “federated” now also describes architectures ranging from distributed
querying and data integration to machine learning, research infrastructures, and collaborative knowledge or data
ecosystems.
Motivated by recurring ambiguity in discussions among co-authors, this poster tries to distinguish prominent
uses 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). We
review 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 on
when and how the term “federated” should be used