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arXiv Machine Learning By R\'emi Bourgerie, \v{S}ar\=unas Girdzijauskas, Viktoria Fodor

From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning

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The paper surveys collaborative learning methods that move beyond traditional Euclidean data to graph-structured data. It reviews foundational principles for Euclidean settings—learning effectiveness, efficiency, and privacy—and then extends the discussion to graph data, presenting a taxonomy of distribution scenarios, statistical heterogeneities, and standardized problem formulations. The survey also outlines open challenges and future research directions in this emerging field.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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