Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add FedAvg MNIST Baseline to README #1552

Merged
merged 2 commits into from
Jan 10, 2023
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 3 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -75,9 +75,11 @@ Stay tuned, more tutorials are coming soon. Topics include **Building Strategies

Flower Baselines is a collection of community-contributed experiments that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas:

* [FedAvg](https://arxiv.org/pdf/1602.05629.pdf):
* [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedavg_mnist)
* [FedBN: Federated Learning on non-IID Features via Local Batch Normalization](https://arxiv.org/pdf/2102.07623.pdf):
* [Convergence Rate](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/fedbn/convergence_rate)
* [Adaptive Federated Optimization](https://arxiv.org/pdf/2003.00295.pdf)
* [Adaptive Federated Optimization](https://arxiv.org/pdf/2003.00295.pdf):
* [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/publications/adaptive_federated_optimization)

Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/using-baselines.html)
Expand Down