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Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships
Official PyTorch implementation for the ICML 2023 paper Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. Our code structure is founded on DomainBed, an out-of-distribution generalization benchmark designed for the centralized scenario, but has been refactored to suit the federated learning.
Requirements
This codebase is written in Python3
, specifically developed using Python 3.8.12
. We utilize PyTorch version 1.10.0
and CUDA version 10.2
. To install the necessary Python packages, you can use:
pip install -r requirements.txt
How to Run
Firstly, download the dataset, such as RotatedMNIST:
python domainbed/download.py --data_dir=./data --dataset RotatedMNIST
Next, train the model. For instance, to execute the FedIIR algorithm on the RotatedMNIST dataset:
python main.py --dataset RotatedMNIST --algorithm FedIIR --num_clients 50 --output_dir train_output
The results will be saved in train_output/RotatedMNIST/FedIIR
, including model files *.pkl
, the result file *.csv
, the standard output file *_stdout
, and the standard error file *_stderr
. It is worth noting that the main file main.py
conducts experiments by treating each domain within the dataset in turn as a test domain once.
The main file main.py
also allows overloading the experiment configuration using parser arguments, some of the important ones are as follows:
--num_clients
: the number of total clients--sample_num
: the number of sampled clients in one communication round--global_epochs
: the number of global communication rounds--local_epochs
: the number of local update epochs--holdout_fraction
: the fraction of each domain set holdout for validation--trial_seed
: the trial number (used for seeding split dataset)--seed
: the seed for everything else
Reference Github
- https://github.com/facebookresearch/DomainBed
- https://github.com/inouye-lab/FedDG_Benchmark
- https://github.com/atuannguyen/FedSR
Citation
Please consider citing our paper as
@InProceedings{pmlr-v202-guo23b,
title = {Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships},
author = {Guo, Yaming and Guo, Kai and Cao, Xiaofeng and Wu, Tieru and Chang, Yi},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
year = {2023}
}