CDKT Algorithm Implementation

This repository implements all experiments for knowledge transfer in federated learning

Running Instruction

Environment: Python 3.8, pytorch

Downloading dependencies

pip3 install -r requirements.txt  

To plot the figure, we use the result files from ./results_fig by running CDKT_plot_summary.py

Main File: CDKT_main.py, Setting File: Setting.py, Plot Summary Results: CDKT_plot_summary.py

PATH: Output: ./results_fig, Figures: ./figs, Dataset: ./data

Modify parameters setting in Setting.py to run the code

Please refer the file Tuning results to access different parameter settings of this work.

-- Example:

CDKT + Mnist + RepFull + Subset of Users + Homogeneous Model: RUNNING_ALGS[1], DATASETS[0], Full_model = False, Rep_Full = True , Subset = True, Same_model = True

CDKT + Cifar-10 + Full + Fixed Users + Heterogeneous Model: RUNNING_ALGS[1], DATASETS[2], Full_model = True, Rep_Full = True , Subset = False, Same_model = False

To run the code, use the command in terminal:

python CDKT_main.py

The Results are stored in ./results_fig and figures in ./figs