## Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views

Code for the paper "Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views".

The dataset exists in the data folder.

The contents of the mask folder are automatically generated by main.py, 
and the result is that obtained by concatenating all the clients data in FMCSC. 
This folder is used as input for the rest of the centralized comparison methods.

To train a new model, run:

```execution
python main.py 
```

Further settings for the dataset, number of clients, multi-view clients / single-view clients, and other parameters can be configured in main.py. 

