docs: federated train readme

+# khu_capstone_1
+
+## 연합학습 기반 유해트래픽 탐지
+- Pytorch
+- CAN protocol 유해 트래픽 데이터 셋
+- FedAvg, FedProx, Fed using timestamp, Fed dynamic weight 논문 구현 및 성능 비교
+
+## Model train
+- Install [PyTorch](http://pytorch.org)
+- Train model
+```bash
+python3 fed_train.py --packet_num 3 --fold_num 0 --batch_size 128 --lr 0.001 --n_nets 100 --comm_type fedprox --comm_round 50
+```

@@ -49,8 +49,6 @@ alpha, beta, gamma = 40.0/100.0, 40.0/100.0, 20.0/100.0
 def add_args(parser):
     parser.add_argument('--packet_num', type=int, default=1,
                         help='packet number used in training, 1 ~ 3')
-    parser.add_argument('--dataset', type=str, default='can',
-                        help='dataset used for training, can or syncan')
     parser.add_argument('--fold_num', type=int, default=0, 
                         help='5-fold, 0 ~ 4')
     parser.add_argument('--batch_size', type=int, default=128,
@@ -549,19 +547,12 @@ def start_train():
     torch.manual_seed(seed)
 
     print("Loading data...")
-    if args.dataset == 'can':
     train_data_set, data_idx_map, net_data_count, test_data_set = dataset.GetCanDataset(args.n_nets, args.fold_num, args.packet_num, "./dataset/Mixed_dataset.csv", "./dataset/Mixed_dataset_1.txt")
-    elif args.dataset == 'syncan':
-        train_data_set, data_idx_map, net_data_count, test_data_set = dataset.GetCanDataset(args.n_nets, args.fold_num, args.packet_num, "./dataset/test_mixed.csv", "./dataset/Mixed_dataset_1.txt")
 
     sampler = dataset.BatchIntervalSampler(len(test_data_set), args.batch_size)
     testloader = torch.utils.data.DataLoader(test_data_set, batch_size=args.batch_size, sampler=sampler,
                                             shuffle=False, num_workers=2, drop_last=True)
 
-    if args.dataset == 'can':
-        fed_model = model.OneNet(args.packet_num)
-        edges = [model.OneNet(args.packet_num) for _ in range(args.n_nets)]
-    elif args.dataset == 'syncan':
     fed_model = model.OneNet(args.packet_num)
     edges = [model.OneNet(args.packet_num) for _ in range(args.n_nets)]