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Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine*

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This research demonstrates that a convolution-based smoothing technique enhances convergence rates in decentralized networks using SVMs.

The proposed method achieves linear convergence when applied to decentralized networks.
Numerical experiments on synthetic and real-world datasets validate the theoretical findings related to optimal statistical convergence.
Utilizing a convolution-based smoothing technique effectively addresses the double nonsmoothness of the hinge loss function.
The finalized sparse estimate recovers the true support of underlying parameters after sufficient ADMM iterations.

Chen et al. (2025) studied this question.

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