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Update content in top-level README #2727

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Update content in top-level README #2727

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jafermarq Dec 19, 2023
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+HeteroFL, +FedAvgM
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17 changes: 13 additions & 4 deletions README.md
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Expand Up @@ -33,8 +33,7 @@ design of Flower is based on a few guiding principles:

- **Framework-agnostic**: Different machine learning frameworks have different
strengths. Flower can be used with any machine learning framework, for
example, [PyTorch](https://pytorch.org),
[TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
example, [PyTorch](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
for users who enjoy computing gradients by hand.

- **Understandable**: Flower is written with maintainability in mind. The
Expand Down Expand Up @@ -80,7 +79,7 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
- [Quickstart (TensorFlow)](https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html)
- [Quickstart (PyTorch)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html)
- [Quickstart (Hugging Face)](https://flower.dev/docs/framework/tutorial-quickstart-huggingface.html)
- [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
- [Quickstart (PyTorch Lightning)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
- [Quickstart (Pandas)](https://flower.dev/docs/framework/tutorial-quickstart-pandas.html)
- [Quickstart (fastai)](https://flower.dev/docs/framework/tutorial-quickstart-fastai.html)
- [Quickstart (JAX)](https://flower.dev/docs/framework/tutorial-quickstart-jax.html)
Expand All @@ -99,11 +98,16 @@ Flower Baselines is a collection of community-contributed projects that reproduc
- [FedMLB](https://github.com/adap/flower/tree/main/baselines/fedmlb)
- [FedPer](https://github.com/adap/flower/tree/main/baselines/fedper)
- [FedProx](https://github.com/adap/flower/tree/main/baselines/fedprox)
- [FedNova](https://github.com/adap/flower/tree/main/baselines/fednova)
- [HeteroFL](https://github.com/adap/flower/tree/main/baselines/heterofl)
- [FedAvgM](https://github.com/adap/flower/tree/main/baselines/fedavgm)
- [FedWav2vec2](https://github.com/adap/flower/tree/main/baselines/fedwav2vec2)
- [FjORD](https://github.com/adap/flower/tree/main/baselines/fjord)
- [MOON](https://github.com/adap/flower/tree/main/baselines/moon)
- [niid-Bench](https://github.com/adap/flower/tree/main/baselines/niid_bench)
- [TAMUNA](https://github.com/adap/flower/tree/main/baselines/tamuna)
- [FedVSSL](https://github.com/adap/flower/tree/main/baselines/fedvssl)
- [FedXGBoost](https://github.com/adap/flower/tree/main/baselines/hfedxgboost)
- [FedPara](https://github.com/adap/flower/tree/main/baselines/fedpara)
- [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
- [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
Expand All @@ -129,14 +133,19 @@ Quickstart examples:
- [Quickstart (XGBoost)](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
- [Quickstart (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
- [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)
- [Quickstart (MLX)](https://github.com/adap/flower/tree/main/examples/quickstart-mlx)
- [Quickstart (XGBoost)](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)

Other [examples](https://github.com/adap/flower/tree/main/examples):

- [Raspberry Pi & Nvidia Jetson Tutorial](https://github.com/adap/flower/tree/main/examples/embedded-devices)
- [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated)
- [Vertical FL](https://github.com/adap/flower/tree/main/examples/vertical-fl)
- [Federated Finetuning of OpenAI's Whisper](https://github.com/adap/flower/tree/main/examples/whisper-federated-finetuning)
- [Comprehensive XGBoost](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
- [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced-tensorflow)
- [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)
- Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation_pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation_tensorflow))
- Single-Machine Simulation of Federated Learning Systems ([PyTorch](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)) ([Tensorflow](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow))
- [Comprehensive Flower+XGBoost](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
- [Flower through Docker Compose and with Grafana dashboard](https://github.com/adap/flower/tree/main/examples/flower-via-docker-compose)

Expand Down
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