Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

This is the PyTorch implemention.

Usage

Setup

We explore data heterogeneity issues in FL with label distribution skew and feature skew.

For experiments with Feature Skew:

Run this basic command:

python main.py --mode [algorithm] --log --dataset [dataset] --save_path checkpoint/[dataset]

For experiments with Feature Skew and Label Distribution Skew:

Run this basic command:

python main.py --mode [algorithm] --log --dataset [dataset] --save_path checkpoint/[dataset] --imbalance_train --beta [beta] --divide [n]

For experiments with Label Distribution Skew:

Run the following basic commands:

cd label-skew

python experiments.py --model=[backbone] --dataset=[dataset] --alg=[algorithm]

Dataset

Benchmark datasets in this work include CIFAR10, CIFAR100, Digits, Office-Caltech10 and DomainNet

For CIFAR10 and CIFAR100 dataset, download and unzip data under 'label-skew/data' file catalog.

For Digits, Office-Caltech10 and DomainNet, put data in 'data' file catalog

Train

Federated Learning

Refer to run.sh for more details