Web: http://arxiv.org/abs/2209.10526

Sept. 22, 2022, 1:12 a.m. | Saeed Vahidian, Mahdi Morafah, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin

cs.LG updates on arXiv.org arxiv.org

Clustered federated learning (FL) has been shown to produce promising results
by grouping clients into clusters. This is especially effective in scenarios
where separate groups of clients have significant differences in the
distributions of their local data. Existing clustered FL algorithms are
essentially trying to group together clients with similar distributions so that
clients in the same cluster can leverage each other's data to better perform
federated learning. However, prior clustered FL algorithms attempt to learn
these distribution similarities indirectly …

arxiv client data distribution federated learning identification

More from arxiv.org / cs.LG updates on arXiv.org

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