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CLoud.py

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+import numpy as np
+from models.Update import *
+from models.Fed import *
+from models.test import *
+from utils.utilis import *
+import copy
+
+def test_with_loss(net_glob, dataset_test, args):
+
+    # testing
+    acc_test, loss_test = test_img(net_glob, dataset_test, args)
+
+    print("Testing accuracy: {:.2f}".format(acc_test))
+
+    return acc_test.item(), loss_test
+
+def Cloud(args, net_glob, dataset_train, dataset_test, dict_users, dict_public):
+    net_glob.train()
+    
+    print("Start Cloud")
+
+    isNext = True
+    today = 1
+    m = 4
+
+    # training
+    acc = []
+    GB = []
+    transf = 161
+    total_transf = 0
+
+    while isNext:
+        if today >= args.physical_time:
+            isNext = False
+        client_train_list = random.sample(
+                    dict_public, m
+                )
+        # print("*" * 80)
+        print("today: {:3d}".format(today))
+        
+        for iter in range(args.epochs):
+
+            w_locals = []
+            lens = []
+            max_time = 0
+            for idx_client in client_train_list: 
+                local = LocalUpdate(
+                    args=args, dataset=dataset_train, idxs=dict_users[idx_client]
+                )
+                w = local.train(net=copy.deepcopy(net_glob).to(args.device))
+
+                w_locals.append(copy.deepcopy(w))
+                lens.append(len(dict_users[idx_client]))
+
+            w_glob = Aggregation(w_locals, lens)
+            # copy weight to net_glob
+            net_glob.load_state_dict(w_glob)
+
+        today += 1
+        total_transf += transf
+        
+        item_acc, item_loss = test_with_loss(net_glob, dataset_test, args)
+        ta, tl = test_with_loss(net_glob, dataset_train, args)
+        acc.append(item_acc)
+        GB.append(total_transf)
+        
+    save_result(acc, "test_acc", args)
+    save_result(GB, "test_GB", args)