Data-Free Knowledge Distillation for Heterogeneous Federated Learning

Research code that accompanies the paper Data-Free Knowledge Distillation for Heterogeneous Federated. It contains implementation of the following algorithms:

• FedGen (the proposed algorithm) (code).
• FedAvg (paper and code).
• FedProx (paper and code).
• FedDistill and its extension FedDistill-FL (paper and code).

Install Requirements:

pip3 install -r requirements.txt

Prepare Dataset:

• To generate non-iid Mnist Dataset following the Dirichlet distribution D(α=0.1) for 20 clients, using 50% of the total available training samples:

cd FedGen/data/Mnist
python generate_niid_dirichlet.py --n_class 10 --sampling_ratio 0.5 --alpha 0.1 --n_user 20
### This will generate a dataset located at FedGen/data/Mnist/u20c10-alpha0.1-ratio0.5/

• Similarly, to generate non-iid EMnist Dataset, using 10% of the total available training samples:

cd FedGen/data/EMnist
python generate_niid_dirichlet.py --sampling_ratio 0.1 --alpha 0.1 --n_user 20 
### This will generate a dataset located at FedGen/data/EMnist/u20-letters-alpha0.1-ratio0.1/

Run Experiments:

There is a main file "main.py" which allows running all experiments.

Run experiments on the Mnist Dataset:

python main.py --dataset Mnist-alpha0.1-ratio0.5 --algorithm FedGen --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3 
python main.py --dataset Mnist-alpha0.1-ratio0.5 --algorithm FedAvg --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3 
python main.py --dataset Mnist-alpha0.1-ratio0.5 --algorithm FedProx --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3 
python main.py --dataset Mnist-alpha0.1-ratio0.5 --algorithm FedDistill-FL --batch_size 32 --num_glob_iters 200 --local_epochs 20 --num_users 10 --lamda 1 --learning_rate 0.01 --model cnn --personal_learning_rate 0.01 --times 3 

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Run experiments on the EMnist Dataset:

python main.py --dataset EMnist-alpha0.1-ratio0.1 --algorithm FedAvg --batch_size 32 --local_epochs 20 --num_users 10 --lamda 1 --model cnn --learning_rate 0.01 --personal_learning_rate 0.01 --num_glob_iters 200 --times 3 
python main.py --dataset EMnist-alpha0.1-ratio0.1 --algorithm FedGen --batch_size 32 --local_epochs 20 --num_users 10 --lamda 1 --model cnn --learning_rate 0.01 --personal_learning_rate 0.01 --num_glob_iters 200 --times 3 
python main.py --dataset EMnist-alpha0.1-ratio0.1 --algorithm FedProx --batch_size 32 --local_epochs 20 --num_users 10 --lamda 1 --model cnn --learning_rate 0.01 --personal_learning_rate 0.01 --num_glob_iters 200 --times 3 
python main.py --dataset EMnist-alpha0.1-ratio0.1 --algorithm FedDistill-FL --batch_size 32 --local_epochs 20 --num_users 10 --lamda 1 --model cnn --learning_rate 0.01 --personal_learning_rate 0.01 --num_glob_iters 200 --times 3 

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Plot

For the input attribute algorithms, list the name of algorithms and separate them by comma, e.g. --algorithms FedAvg,FedGen,FedProx

  python main_plot.py --dataset EMnist-alpha0.1-ratio0.1 --algorithms FedAvg,FedGen --batch_size 32 --local_epochs 20 --num_users 10 --num_glob_iters 200 --plot_legend 1