Paper: Distributed learning with non-smooth objective functions
We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network.
We employ a zerothorder method to minimize the associated augmented Lagrangian in the primal domain using the alternating direction method of multipliers (ADMM) to develop the proposed algorithm, named distributed zeroth-order based ADMM (D-ZOA).
Unlike most existing algorithms for non-smooth optimization, which rely on calculating subgradients or proximal operators, D-ZOA only requires function values to approximate gradients of the objective function. Convergence of D-ZOA to the centralized solution is confirmed via theoretical analysis and simulation results.
Cristiano Gratton, Naveen K. D. Venkategowda, Reza Arablouei, Stefan Werner. Distributed Learning with Non-Smooth Objective Functions. European Signal Processing Conference. Jan 2021.
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