- Improving the Documentation part and docstrings of the code.
- Adding some other datasets and models.
- Adding a test suite using pytest
- Thoughts on Checkpointing.
- Adding commands to deal with data downloading and/or preprocessing?
- Integration of Differential Privacy.
- Integration of FHE
- Adding other strategies:
- FedProx
- ...
- Refacto: splitting flower directory into flower and typer parts. Separating commands themselves from code of Flower.
- Better logging: fusion between logging through Rich Console and logging from Flower?
- Use Pydantic to control config files and the good use of the different tools.
- Adding the possibility to save the weights of the model. Cane be done using Fabric.
- Working on Docker part.
- Using only LightningDataModule, and getting rid of load_data.
- Logging with tensorboard.
Here is a list of more mid/long term ideas to implement in Pybiscus for Federated Learning.