Merge branch 'main' into jafermarq-update-baseline-request-template

README.md

@@ -91,18 +91,26 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
 
 ## Flower Baselines
 
-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/abs/1602.05629):
-  - [MNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
-- [FedProx](https://arxiv.org/abs/1812.06127):
-  - [MNIST](https://github.com/adap/flower/tree/main/baselines/fedprox/)
-- [Adaptive Federated Optimization](https://arxiv.org/abs/2003.00295):
-  - [CIFAR-10/100](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
-
-Check the Flower documentation to learn more: [Using Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-
-The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline: [Contributing Baselines](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+Flower Baselines is a collection of community-contributed projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!
+
+- [DASHA](https://github.com/adap/flower/tree/main/baselines/dasha)
+- [DepthFL](https://github.com/adap/flower/tree/main/baselines/depthfl)
+- [FedBN](https://github.com/adap/flower/tree/main/baselines/fedbn)
+- [FedMeta](https://github.com/adap/flower/tree/main/baselines/fedmeta)
+- [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)
+- [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)
+- [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)
+
+Please refer to the [Flower Baselines Documentation](https://flower.dev/docs/baselines/) for a detailed categorization of baselines and for additional info including:
+* [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
+* [How to contribute a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
 
 ## Flower Usage Examples
 

baselines/baseline_template/README.md

@@ -1,11 +1,11 @@
 ---
 title: title of the paper
 url: URL to the paper page (not the pdf)
-labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo")
-dataset: [dataset1, dataset2] # list of datasets you include in your baseline
+labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. system heterogeneity, image classification, asynchronous, weight sharing, cross-silo). Do not use ""
+dataset: [dataset1, dataset2] # list of datasets you include in your baseline. Do not use ""
 ---
 
-# :warning:*_Title of your baseline_*
+# :warning: *_Title of your baseline_*
 
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
@@ -15,33 +15,33 @@ dataset: [dataset1, dataset2] # list of datasets you include in your baseline
 
 > :warning: Please complete the metadata section at the very top of this README. This generates a table at the top of the file that will facilitate indexing baselines.
 
-****Paper:**** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
+**Paper:** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
 
-****Authors:**** :warning: *_list authors of the paper_*
+**Authors:** :warning: *_list authors of the paper_*
 
-****Abstract:**** :warning: *_add here the abstract of the paper you are implementing_*
+**Abstract:** :warning: *_add here the abstract of the paper you are implementing_*
 
 
 ## About this baseline
 
-****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
+**What’s implemented:** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
 
-****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
+**Datasets:** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
 
-****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
+**Hardware Setup:** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
 
-****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
+**Contributors:** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
 
 
 ## Experimental Setup
 
-****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
+**Task:** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
 
-****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
+**Model:** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
 
-****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
+**Dataset:** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
 
-****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
+**Training Hyperparameters:** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
 
 
 ## Environment Setup

baselines/baseline_template/baseline_template/main.py

@@ -51,3 +51,7 @@ def main(cfg: DictConfig) -> None:
     # Hydra will generate for you a directory each time you run the code. You
     # can retrieve the path to that directory with this:
     # save_path = HydraConfig.get().runtime.output_dir
+
+
+if __name__ == "__main__":
+    main()