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A Novel Federated Multi-View Clustering Method for Unaligned and Incomplete Data Fusion

Code for the paper "A Novel Federated Multi-View Clustering Method for Unaligned and Incomplete Data Fusion".

Requirements

The code requires:

• Python 3.6 or higher

• Pytorch 1.9 or higher

We use the Flower federated learing framework for all client-server implementation. Flower and other dependencies can be installed via following command:

pip install -r requirements.txt

Example execution

First use the following command to setup the dataset of your choice (e.g., Scene) for any number of clients (e.g., 3):

python sampler.py --dataset="Scene" --n_clients=3
python sampler.py --dataset="Scene" --n_clients=3 --missing=0.5

Then, to train a new model, run:

python main.py 

Further settings for the dataset, number of clients, overlapping rate, align_rate, and other parameters can be configured in config.py.

Citation

If you find our code useful, please cite:

Yazhou Ren, Xinyue Chen, Jie Xu, Jingyu Pu, Yonghao Huang, Xiaorong Pu, Ce Zhu, Xiaofeng Zhu, Zhifeng Hao, and Lifang He. A novel federated multi-view clustering method for unaligned and incomplete data fusion. Information Fusion, page 102357, 2024.

Thanks. Any problem can contact Xinyue Chen (martinachen2580@gmail.com).