更新配置文件，移除 YOLO 版本实现计划，调整 UAV 和 COCO 配置中的数据量和批次大小

README.md

@@ -19,9 +19,6 @@ nohup bash fed_run.sh 1 > train.log 2>&1 &
     - Implement FedProx
     - Implement SCAFFOLD
     - Implement FedNova
-- Add more YOLO versions (e.g., YOLOv8, YOLOv5, etc.)
-    - Implement YOLOv8
-    - Implement YOLOv5
 
 # references
 [PyTorch Federated Learning](https://github.com/rruisong/pytorch_federated_learning)

config/coco_cfg.yaml

@@ -17,8 +17,8 @@ local_batch_size: 32              # local training batch size
 val_batch_size: 128               # validation batch size
 
 num_workers: 8                    # number of data loader workers
-min_data: 1700                    # minimum number of images per client
-max_data: 1800                    # maximum number of images per client
+min_data: 1800                    # minimum number of images per client
+max_data: 1900                    # maximum number of images per client
 partition_mode: "overlap"         # "overlap" or "disjoint"
 connection_ratio: 1               # connection ratio, e.g., 1.0 means all clients
 

config/uav_cfg.yaml

@@ -3,22 +3,22 @@ fed_algo: "FedAvg"                # federated learning algorithm
 model_name: "yolo_v11_n"          # yolo_v11_n, yolo_v11_t, yolo_v11_s, yolo_v11_m, yolo_v11_l, yolo_v11_x
 i_seed: 202509                    # initial random seed
 
-num_client: 100                   # total number of clients
-num_round: 500                    # total number of communication rounds
+num_client: 36                    # total number of clients
+num_round: 50                     # total number of communication rounds
 num_local_class: 1                # number of classes per client
 
 res_root: "results"               # root directory for results
-dataset_path: "/home/image1325/ssd1/dataset/uav/"
+dataset_path: "/mnt/DATA/uav/"
 # train_txt: "train.txt"            # path to training set txt file
 # val_txt: "val.txt"                # path to validation set txt file
 # test_txt: "test.txt"              # path to test set txt file                 
 
-local_batch_size: 32              # local training batch size
-val_batch_size: 16                # validation batch size
+local_batch_size: 36               # local training batch size
+val_batch_size: 128                # validation batch size
 
-num_workers: 4                    # number of data loader workers
-min_data: 640                     # minimum number of images per client
-max_data: 720                     # maximum number of images per client
+num_workers: 8                    # number of data loader workers
+min_data: 385                     # minimum number of images per client
+max_data: 400                     # maximum number of images per client
 partition_mode: "overlap"         # "overlap" or "disjoint"
 connection_ratio: 1               # connection ratio, e.g., 1.0 means all clients