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Implementation of the paper No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data

Run this repo:

  1. Download the cifar10 dataset and save as images in the dir "./data/"

    python data_process.py

  2. Run the main procedure:

    python main.py

  3. Run t-SNE visualization:

    python visualize.py [--model_before_calibration MODEL_BEFORE_CALIBRATION] [--model_after_calibration MODEL_AFTER_CALIBRATION] [--random_state RANDOM_STATE] [--save_path SAVE_PATH]

    Default arguments are:

    • MODEL_BEFORE_CALIBRATION: ./save_model/model-epoch9.pth
    • MODEL_AFTER_CALIBRATION: ./save_model/model.pth
    • RANDOM_STATE: 1
    • SAVE_PATH: ./visualize/tsne.png