a simple MLP model was considered and the initial model() shared between workers.
Conventionally, each worker train model based on its own dataset and shared the resulted model. By averaging models from different workers, the new model achieved for the next round of algorithms.
How efficiently share gradients of different workers and averaged them and use the “optimizer .step()” to update the initial model(initial model shared among workers)?