VFedPCA+VFedAKPCA

This is the official source code for the Paper: Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data based on Pytorch Framework.

Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting. This paper will study the unsupervised FL under the vertically partitioned dataset setting.

Server-Clients Architecture

Server-Clients Architecture

Figure 1.: Server-Clients Architecture

Master Branch

VFedPCA+VFedAKPCA                    
└── case                        // Case Studies
    └── figs                    // Save experimental results' figures in '.eps' / '.png' format 
        ├── img_name*.eps              
        └── img_name*.png           
    ├── main.py          
    ├── model.py              
    └── utils.py                 
├── dataset                     // Put downloaded dataset in this folder
└── figs                        // Save experimental results' figures in '.eps' / '.png' format
    ├── img_name*.eps              
    └── img_name*.png           
├── README.md               
├── main.py                     // Experiment on Structured Dataset
├── model.py                   
└── utils.py

Environments

  • python = 3.8.8
  • numpy = 1.20.1
  • pandas = 1.2.4
  • scipy = 1.6.2
  • imageio = 2.9.0

Prepare Dataset

To demonstrate the superiority of our method, we utilized FIVE types of real-world datasets coming with distinct nature.

  1. structured datasets from different domains;
  2. medical image dataset;
  3. face image dataset;
  4. gait image dataset;
  5. person re-identification image dataset.

Step 1: Download Dataset from the Google Drive URL

Step 2: Specify Dataset Path by Command Argument

$ python main.py --data_path="./dataset/xxx"

Experiments

We conduct extensive experiments on structured datasets to exmaines the effect of feature size, local iterations, warm-start power iterations, and weight scaling method on structed datasets. Furthermore, we investigate some case studies with image dataset to demonstrate the effectiveness of VFedPCA and VFedAKPCA.

A. Experiment on Structured Dataset

First, you need to choose the dataset.

$ python main.py --data_path './dataset/College.csv' --batch_size 160

Then, you only need to set different flag, p_list, iter_list and sampler_num to exmaines the effect of feature size, local iterations, warm-start power iterations, and weight scaling method on structed datasets. The example is as follows.

# 'clients': the effect of local feature size; 
# 'iterations': the effect of local iterations; 
# 'warmstart': The effect of warm-start power iterations.

flag ='clients' 
p_list = [3, 5, 10]         # the number of involved clients
iter_list = [100, 100, 100] # the number of local power iterations
sampler_num = 5

B. Case Studies

$ cd case                   # change into case folder
$ python main.py --data_path '../dataset/Image/DeepLesion' /
               --client_num 8 / 
               --iterations 100 / 
               --re_size 512

Demo Visualization

The final results of comparative experiment on image datasets: YaleFace (center-light), CasiaGait (sequence 1) and DeepLesion with algorithms: (a) PCA: the un-split data, (b) VFedPCA: the split data, (c) PCA: the isolated data, (d) the un-split data, (e) the federated data, (f) the isolated data.

Server-Clients Architecture

Figure 2.: Server-Clients Architecture

Citation

@inproceedings{
title = {{Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data}},
author = {Yiu-ming Cheung, Fellow, IEEE, Feng Yu, and Jian Lou},
year = 2021
}

GitHub

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