Gradient descent is a widely used paradigm for solving many optimization problems. Gradient descent aims to minimize a target function in order to reach a local minimum. In machine learning or data ...

Distributed stochastic gradient descent (SGD) has attracted considerable recent attention due to its potential for scaling computational resources, reducing training time, and helping protect user ...