  First-order optimization methods, such as stochastic gradient descent (SGD)
and its variants, are widely used in machine learning applications due to their
simplicity and low per-iteration costs. However, they often require larger
numbers of iterations, with associated communication costs in distributed
environments. In contrast, Newton-type methods, while having higher
per-iteration costs, typically require a significantly smaller number of
iterations, which directly translates to reduced communication costs. In this
paper, we present a novel distributed optimizer for classification problems,
which integrates a GPU-accelerated Newton-type solver with the global consensus
formulation of Alternating Direction of Method Multipliers (ADMM). By
leveraging the communication efficiency of ADMM, GPU-accelerated inexact-Newton
solver, and an effective spectral penalty parameter selection strategy, we show
that our proposed method (i) yields better generalization performance on
several classification problems; (ii) significantly outperforms
state-of-the-art methods in distributed time to solution; and (iii) offers
better scaling on large distributed platforms.

Machine Learning;Machine Learning