Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization

Demidovich Y.; Petr Ostroukhov; Malinovsky G.; Horvath S.; Takáč M.; Richtárik P.; E. Gorbunov

doi:10.48550/arXiv.2412.02781

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Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization

Demidovich Y., Petr Ostroukhov, Malinovsky G., Horvath S., Takáč M., Richtárik P., Горбунов Э. А.

Язык: английский

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Ключевые слова: distributed optimization Federated learning Optimizations Random Reshuffling

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This article is devoted to the implementation of the federated approach to named entity recognition. The novel federated approach is designed to solve data privacy issues. The classic BiLSTM-CNNs-CRF and its modifications trained on a single machine are taken as baseline. Federated training is conducted for them. Influence of use of pretrained embedding, use of ...

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We study the complexity of approximating the Wasserstein barycenter of m discrete measures, or histograms of size n, by contrasting two alternative approaches that use entropic regularization. The first approach is based on the Iterative Bregman Projections (IBP) algorithm for which our novel analysis gives a complexity bound proportional to $m n^2 / \epsilon^2$ to approximate the original non-regularized barycenter. ...

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