Resource-Adaptive Newton’s Method for Distributed Learning

By leveraging curvature information for improved performance, Newton’s method offers significant advantages over first-order methods for distributed learning problems. However, the practical applicability of Newton’s method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called Resource-Adaptive Newton Learning (RANL), which overcomes the limitations of Newton’s method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Moreover, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed learning in practical scenarios.