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FedMoP: Federated Learning using Model Projection for Multi‑Center Disease Diagnosis with Non-IID data (Under review)

This is the PyTorch implementation of our paper "Federated Learning using Model Projection for Multi-Center Disease Diagnosis with Non-IID Data" .

The experimental results (test accuracy %) on HAM10K dataset.

Methods		Two-client	Four-client
Centralized learning		68.94±0.06
Federated learning	FedAvg(AISat-2017)	65.43±0.29	59.22±0.77
	FedProx(PMLS-2020)	66.25±0.29	59.22±0.29
	SCAFFOLD(ICML-2020)	67.08±1.34	64.39±0.29
	FedaGrac(TPDS-2023)	67.70±0.88	64.18±0.59
	FedReg(ICLR-2022)	66.67±1.05	65.84±0.05
	Our FedMoP	69.36±0.30 (↑ 1.66)	69.57±0.51 (↑ 3.73)

Usage

requirements：

• Ubuntu Server == 20.04.4 LTS
• CUDA == 11.6
• numpy ==1.23.1
• Pillow == 9.2.0
• python == 3.8.0
• quadprog == 0.1.11
• torch == 1.12.0
• torchvision == 0.13.0

Datasets：

• HAM10k：Please manually download the HAM10000 dataset from the official website, unzip it and place it in './dataset/ham10k'.
• COVID-19：Please manually download the COVID-19 Five-classes dataset from the official website, unzip it and place it in './dataset/covid19'.
• PBC: Please manually download the PBC dataset from the official website, unzip it and place it in './dataset/pbc'.

Split the dataset in feature/label distribution skew：

You can use split_dataset.py to split the dataset into feature distribution skew and label distribution skew, and then save the split data set for model training.

Training:

main.py is the main file to run the federated experiments.

The experiments can be run by:

cd FedMoP
python main.py