Add Flower Baseline: FedGen

Paper

Zhu, Zhuangdi & Hong, Junyuan & Zhou, Jiayu. (2021). Data-Free Knowledge Distillation for Heterogeneous Federated Learning.

Link

https://arxiv.org/abs/2105.10056

Maybe give motivations about why the paper should be implemented as a baseline.

Proposes a data-free knowledge distillation approach to address heterogeneous FL, where the server learns a lightweight generator to ensemble user information in a data-free manner, which is then sent to the users to regulate local training using the learned knowledge as an inductive bias.

Achieves better generalization performance compared with the state-of-the-art.

Is there something else you want to add?

No response

Implementation

To implement this baseline, it is recommended to do the following items in that order:

For first time contributors

[x] Read the first contribution doc
[x] Complete the Flower tutorial
[x] Read the Flower Baselines docs to get an overview:
- [x] How to use Flower Baselines
- [x] How to contribute a Flower Baseline

Prepare - understand the scope

[ ] Read the paper linked above
[ ] Decide which experiments you'd like to reproduce. The more the better!
[ ] Follow the steps outlined in Add a new Flower Baseline.
[ ] You can use as reference other baselines that the community merged following those steps.

Verify your implementation

[ ] Follow the steps indicated in the EXTENDED_README.md that was created in your baseline directory
[ ] Ensure your code reproduces the results for the experiments you chose
[ ] Ensure your README.md is ready to be run by someone that is no familiar with your code. Are all step-by-step instructions clear?
[ ] Ensure running the formatting and typing tests for your baseline runs without errors.
[ ] Clone your repo on a new directory, follow the guide on your own README.md and verify everything runs.

adap / flower