Web: http://arxiv.org/abs/2201.11036

Jan. 27, 2022, 2:11 a.m. | Giacomo Verardo, Daniel Barreira, Marco Chiesa, Dejan Kostic

cs.LG updates on arXiv.org arxiv.org

In cross-device Federated Learning (FL), clients with low computational power
train a common machine model by exchanging parameters updates instead of
potentially private data. Federated Dropout (FD) is a technique that improves
the communication efficiency of a FL session by selecting a subset of model
variables to be updated in each training round. However, FD produces
considerably lower accuracy and higher convergence time compared to standard
FL. In this paper, we leverage coding theory to enhance FD by allowing a …

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