FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs. (arXiv:2208.13685v1 [cs.LG])

As special information carriers containing both structure and feature
information, graphs are widely used in graph mining, e.g., Graph Neural
Networks (GNNs). However, in some practical scenarios, graph data are stored
separately in multiple distributed parties, which may not be directly shared
due to conflicts of interest. Hence, federated graph neural networks are
proposed to address such data silo problems while preserving the privacy of
each party (or client). Nevertheless, different graph data distributions among
various parties, which is known …

ego privacy