修改聚合逻辑，每次聚合不再创建新模型

fed_algo_cs/server_base.py

@@ -121,40 +121,53 @@ class FedYoloServer(object):
         return {"mAP": float(mean_ap), "mAP50": float(map50), "precision": float(prec), "recall": float(rec)}
 
     def select_clients(self, connection_ratio=1.0):
-        """Randomly select a fraction of clients."""
+        """
+        Randomly select a fraction of clients.
+        Args:
+            connection_ratio: fraction of clients to select (0 < connection_ratio <= 1)
+        """
         self.selected_clients = []
         self.n_data = 0
         for client_id in self.client_list:
+            # Random selection based on connection ratio
             if np.random.rand() <= connection_ratio:
                 self.selected_clients.append(client_id)
                 self.n_data += self.client_n_data.get(client_id, 0)
 
     def agg(self):
-        """Aggregate client updates (FedAvg)."""
+        """
+        Aggregate client updates (FedAvg).
+        Returns:
+            global_state: aggregated model state dictionary
+            avg_loss: dict of averaged losses
+            n_data: total number of data classes samples used in this round
+        """
         if len(self.selected_clients) == 0 or self.n_data == 0:
             return self.model.state_dict(), {}, 0
 
-        model = init_model(self.model_name, self._num_classes)
-        model_state = model.state_dict()
+        # start from current global model
+        global_state = self.model.state_dict()
+
+        # zero buffer for accumulation
+        new_state = {k: torch.zeros_like(v, dtype=torch.float32) for k, v in global_state.items()}
 
         avg_loss = {}
-        for i, name in enumerate(self.selected_clients):
+        for name in self.selected_clients:
             if name not in self.client_state:
                 continue
             weight = self.client_n_data[name] / self.n_data
-            for key in model_state.keys():
-                if i == 0:
-                    model_state[key] = self.client_state[name][key] * weight
-                else:
-                    model_state[key] += self.client_state[name][key] * weight
+            for k in new_state.keys():
+                # accumulate in float32 to avoid fp16 issues
+                new_state[k] += self.client_state[name][k].to(torch.float32) * weight
 
-            # Weighted average losses
+            # losses
             for k, v in self.client_loss[name].items():
                 avg_loss[k] = avg_loss.get(k, 0.0) + v * weight
 
-        self.model.load_state_dict(model_state, strict=True)
+        # load aggregated params back into global model
+        self.model.load_state_dict(new_state, strict=True)
         self.round += 1
-        return model_state, avg_loss, self.n_data
+        return self.model.state_dict(), avg_loss, self.n_data
 
     def rec(self, name, state_dict, n_data, loss_dict):
         """