Abstract
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 propose FedSparsify, which performs model pruning during federated training. In our experiments in centralized and federated settings on the brain age prediction task (estimating a person’s age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.
D. Stripelis and U. Gupta—Equal contribution.
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Notes
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Communication cost is computed as \(\sum _t^T 2 N_Z^t L \). T represents the total number of federation rounds, \(N_Z^t\) the non-zero model parameters at round t and L the number of participating learners. Factor 2 accounts for the model parameters sent from the controller to the learners and from the learners to the controller within a round.
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Stripelis, D., Gupta, U., Dhinagar, N., Steeg, G.V., Thompson, P.M., Ambite, J.L. (2022). Towards Sparsified Federated Neuroimaging Models via Weight Pruning. In: Albarqouni, S., et al. Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health. DeCaF FAIR 2022 2022. Lecture Notes in Computer Science, vol 13573. Springer, Cham. https://doi.org/10.1007/978-3-031-18523-6_14
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