Graph Learning Across Data Silos

arXiv:2301.06662v3 Announce Type: replace
Abstract: We consider the problem of inferring graph topology from smooth graph signals in a novel but practical scenario where data are located in distributed clients and prohibited from leaving local clients due to factors such as privacy concerns. The main difficulty in this task is how to exploit the potentially heterogeneous data of all clients under data silos. To this end, we first propose an auto-weighted multiple graph learning model to jointly learn a personalized …

abstract arxiv concerns cs.cr cs.lg data data silos distributed eess.sp exploit graph graph learning novel practical privacy topology type