FedST

A federated image segmentation method based on style transfer

Implementation of the paper accepted by AAAI 2024: FedST: Federated Style Transfer Learning for Non-IID image segmentation

Abstract: Federated learning collaboratively trains machine learning models among different clients while keeping data privacy and has become the mainstream for breaking data silos. However, the non-independently and identically distribution (i.e., Non-IID) characteristic of different image domains among different clients reduces the benefits of federated learning and has become a bottleneck problem restricting the accuracy and generalization of federated models. In this work, we propose a novel federated image segmentation method based on style transfer, FedST, by using a denoising diffusion probabilistic model to achieve feature disentanglement and image synthesis of cross-domain image data between multiple clients. Thus it can share style features among clients while protecting structure features of image data, which effectively alleviates the influence of the Non-IID phenomenon.

Overview of the proposed federated style transfer： The FedST-separate and FedST-join are two variants. The former lets each client trains a unique style transfer generator and constructs a unified style store to save them. And it exchanges generators to let each client generate cross-domain data using their own local label. While the latter is equipped with a global controllable module to train a unified style transfer generator around all clients using the FedAvg method. And each client can modify the domain vector to generate cross-domain data. Finally, both of them use FedAvg to train the target image segmentation model using local and synthetic data.

Dependencies & Environment

This experiment was conducted in the following environment and platform, while other environments and platforms have not been tested.

• Python=3.6.9

• Pytorch=1.8.1

• Platform: Tesla V100-SXM2-32GB

Usage

• Prepare your dataset and place them under the dataset folder. The file structure is similar to the following shown

  Name of your dataset
  ├─test
  │  ├─real_image
  │  │  ├─0
  │  │  └─1
  │  └─real_label
  │      ├─0
  │      └─1
  └─train
      ├─fake_image
      │  ├─0
      │  └─1
      ├─real_image
      │  ├─0
      │  └─1
      └─real_label
          ├─0
          └─1
  

• Train style transfer generator to generate Synthetic Cross-Domain Data and place them under the train/fake_image folder as shown above.

FedST-Separate: reference: https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models

FedST-Join: reference: ./scripts/train_test.sh

• Train and Test Federated Learning Segmentation Model

Reference: ./scripts/train_test.sh

Citation