All clients are sharing the same underlying learner.

[ShiehShieh]

FedProx/flearn/trainers/fedbase.py

Line 17 in d2a4501

self.clients = self.setup_clients(dataset, self.client_model)

Please take a look at this line. It seems that all clients are using the same ML model for local training. In other words, there is no local model, but a global model which is sequentially trained on each client.

This can be verified by the following code snippet (I have tested it on flearn/trainers/fedavg.py).

            csolns = []  # buffer for receiving client solutions

            lastc = None
            for idx, c in enumerate(active_clients.tolist()):  # simply drop the slow devices
                print(i, idx)
                if lastc is not None:
                  for j in range(len(lastc)):
                    print('Is the parameters of the current client (before training) the same as the parameters of the previous client (after training)?: %s' % (c.get_params()[j] == lastc[j]).all())
                  from time import sleep
                  sleep(1)
                else:
                  print('The first client.')
                # communicate the latest model
                c.set_params(self.latest_model)

                # solve minimization locally
                soln, stats = c.solve_inner(num_epochs=self.num_epochs, batch_size=self.batch_size)
                lastc = c.get_params()

                # gather solutions from client
                csolns.append(soln)

                # track communication cost
                self.metrics.update(rnd=i, cid=c.id, stats=stats)

            # update models
            self.latest_model = self.aggregate(csolns)

In my opinion, this is not expected for federated learning.