🐞 fix(pFedSim): Empty self.client_trainable_params at server side

Due to no tracking gradients anymore for tensors in self.global_params_dict, the initialization of self.client_trainable_params needs to be changed.

Also delete the arguments `unique_model` and `default_trainer` that pass to `super.__init__()`, which means these two args should be decided by pFedSim's parent class.

src/server/pfedsim.py

@@ -16,16 +16,10 @@ def get_pfedsim_argparser() -> ArgumentParser:
 
 
 class pFedSimServer(FedAvgServer):
-    def __init__(
-        self,
-        algo: str = "pFedSim",
-        args: Namespace = None,
-        unique_model=False,
-        default_trainer=True,
-    ):
+    def __init__(self, algo: str = "pFedSim", args: Namespace = None):
         if args is None:
             args = get_pfedsim_argparser().parse_args()
-        super().__init__(algo, args, unique_model, default_trainer)
+        super().__init__(algo, args)
         self.test_flag = False
         self.weight_matrix = torch.eye(self.client_num, device=self.device)
 
@@ -56,10 +50,13 @@ def train(self):
             console=self.logger.stdout,
         )
         self.trainer.personal_params_name.extend(
-            [name for name in self.model.state_dict() if "classifier" in name]
+            [name for name in self.model.state_dict().keys() if "classifier" in name]
         )
         self.client_trainable_params = [
-            trainable_params(self.global_params_dict, detach=True)
+            [
+                self.global_params_dict[key]
+                for key in trainable_params(self.model, requires_name=True)[1]
+            ]
             for _ in self.train_clients
         ]
 
@@ -76,16 +73,14 @@ def train(self):
             client_params_cache = []
             for client_id in self.selected_clients:
                 client_pers_params = self.generate_client_params(client_id)
-                (
-                    client_params,
-                    _,
-                    self.client_metrics[client_id][E],
-                ) = self.trainer.train(
-                    client_id=client_id,
-                    local_epoch=self.clients_local_epoch[client_id],
-                    new_parameters=client_pers_params,
-                    return_diff=False,
-                    verbose=((E + 1) % self.args.verbose_gap) == 0,
+                (client_params, _, self.client_metrics[client_id][E]) = (
+                    self.trainer.train(
+                        client_id=client_id,
+                        local_epoch=self.clients_local_epoch[client_id],
+                        new_parameters=client_pers_params,
+                        return_diff=False,
+                        verbose=((E + 1) % self.args.verbose_gap) == 0,
+                    )
                 )
                 client_params_cache.append(client_params)