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A Mutual Information Perspective on Federated Contrastive Learning
May 6, 2024, 4:42 a.m. | Christos Louizos, Matthias Reisser, Denis Korzhenkov
cs.LG updates on arXiv.org arxiv.org
Abstract: We investigate contrastive learning in the federated setting through the lens of SimCLR and multi-view mutual information maximization. In doing so, we uncover a connection between contrastive representation learning and user verification; by adding a user verification loss to each client's local SimCLR loss we recover a lower bound to the global multi-view mutual information. To accommodate for the case of when some labelled data are available at the clients, we extend our SimCLR variant …
abstract arxiv client cs.lg information lens loss perspective representation representation learning through type verification view
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