Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning

We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the alternating direction method of multipliers. The proposed algorithm is fully-distr...

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Bibliographic Details
Main Authors: Gratton, Cristiano, Kumar Dasanadoddi Venkategowda, Naveen, Arablouei, Reza, Werner, Stefan
Format: Book
Language:English
Online Access:Request full text
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Summary:We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the alternating direction method of multipliers. The proposed algorithm is fully-distributed and does not require the conjugate function of any non-smooth regularizer function, which may be unfeasible or computationally inefficient to acquire. Numerical experiments demonstrate the effectiveness of the proposed algorithm.