#Visual Prompt Based Personalized Federated Learning(TMLR 2024) This repository contains the official code for our proposed method,This paper has been accepted at TMLR 2024.

Environments

With the installed conda, we can run this platform in a conda virtual environment called fl_torch. Note: due to the code updates, some modules are required to install based on the given *.yaml.

conda env create -f env_linux.yaml # for Linux

Datasets

I currently using three famous datasets: Cifar10, Cifar100 and Tiny-ImageNet (Please download the original data from http://cs231n.stanford.edu/tiny-imagenet-200.zip).

they can be easy split into IID and Non-IID version with ./dataset/utils/dataset_utils.py. It is easy to add other datasets to this FL platform.

In Non-IID setting, two situations exist. The first one is the pathological Non-IID setting, the second one is practical Non-IID setting. In the pathological Non-IID setting, for example, the data on each client only contains the specific number of labels (maybe only two labels), though the data on all clients contains 10 labels such as MNIST dataset. In the practical Non-IID setting, Dirichlet distribution is utilized (please refer to this paper for details). We can input balance for the iid setting, where the data are uniformly distributed.

Examples for Cifar10

• Cifar10

  cd ./dataset
  python -u generate_cifar10.py iid - - 50 1 10 # for iid and balanced setting
  # python -u generate_cifar10.py noniid - dir 50 0.3 10 # for practical noniid and unbalanced setting with 50 clients and dir $\alpha$ is 0.3
  # python generate_mnist.py noniid - pat 50 1 5 # for pathological noniid and unbalanced setting with 50 clients and each client possesses 5 classes
  

How to start simulating

• Build dataset: Datasets

• Train and evaluate the model:

  cd ./system
  python -u main.py  --arv1 "iid" --arv2 "-" --arv3 "-" --arv4  "50" --arv5  "1" --arv6  "10"  --device_id  0 -algo PFedPT  -m cnn -lbs 16 -nc 50 -jr 0.2   -pls 5 -gr 150 -ls 5 -np 2 -data "Cifar10" -nb 10 --pt_learning_rate 1  # for pFedPT and Cifar10
  

  Or you can uncomment the lines you need in ./system/auto_train.sh and run:

  cd ./system
  sh auto_train.sh
  

Note: The hyper-parameters have not been tuned for the algorithms. The values in ./system/auto_train.sh are just examples. You need to tune the hyper-parameters by yourself.

Acknowledgements

Much of the code in this repository was adapted from code in this repository PFLlib, Please refer to their repository for more detailed information.. Their work has greatly contributed to my research.