SortedNet's stochastic updating scheme can have a significant impact on the convergence rate of neural networks compared to traditional methods. By randomly sampling sub-models and updating them in each iteration, SortedNet introduces a level of stochasticity that allows for more efficient exploration of the parameter space. This randomness helps prevent getting stuck in local minima and promotes faster convergence towards an optimal solution. Additionally, by training multiple sub-models simultaneously with shared parameters, SortedNet leverages the modularity of deep neural networks to enhance overall performance.