Towards Sparsified Federated Neuroimaging Models via Weight Pruning. (arXiv:2208.11669v1 [cs.LG])

Federated training of large deep neural networks can often be restrictive due
to the increasing costs of communicating the updates with increasing model
sizes. Various model pruning techniques have been designed in centralized
settings to reduce inference times. Combining centralized pruning techniques
with federated training seems intuitive for reducing communication costs -- by
pruning the model parameters right before the communication step. Moreover,
such a progressive model pruning approach during training can also reduce
training times/costs. To this end, we …

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