Parallel computing support

[JackingChen]

Hi thanks for providing this wonderful repository, but I'm wondering if there will be support for parallelization of client training in each round

specifically, making the local update in federated_main.py to be executed by parallel processes

for idx in idxs_users:
            local_model = LocalUpdate(args=args, dataset=train_dataset,
                                      idxs=user_groups[idx], logger=logger)
            w, loss = local_model.update_weights(
                model=copy.deepcopy(global_model), global_round=epoch)
            local_weights.append(copy.deepcopy(w))
            local_losses.append(copy.deepcopy(loss))

or

are there suggestions for start working on this approach?

[saigontrade88]

parallelization of client training in each round

I also have the same question. Thanks.

[Xiaoni-61]

It's not possible now, but you can use threading to do it