feat(baselines): Add FedSaSync strategy

baselines/fedsasync/LICENSE

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baselines/fedsasync/README.md

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+---
+title: "Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment"
+url: https://arxiv.org/abs/2606.24230
+labels: [Federated Learning, Semi-Asynchronous, System Heterogeneity, Flower]
+dataset: [CIFAR10, MNIST]
+---
+# FedSaSync: Semi-asynchronous Federated Learning in Flower
+
+> 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.
+
+**Paper:** [arxiv.org/abs/2606.24230](https://arxiv.org/abs/2606.24230)
+
+**Authors:** Víctor Hidalgo-Izquierdo, Carmen Carrión, Blanca Caminero
+
+**Abstract:** This paper presents an extension of the Flower federated learning framework to support Semi-Asynchronous Federated Learning. The proposed approach adapts the traditional synchronous paradigm to better handle client heterogeneity and straggler effects. By introducing a semi-asynchronous training strategy, the system allows partial synchronization among clients while maintaining training efficiency and scalability. We implement and evaluate the proposed modification within Flower, instantiated as the FedSaSync strategy, demonstrating improved robustness and reduced idle time compared to fully synchronous baselines in heterogeneous environments. The results show that SAFL can balance convergence stability and system efficiency in heterogeneous environments typical of edge and distributed learning scenarios. 
+
+
+## About this baseline
+
+**What’s implemented:** The code in this directory is used to execute the experiments proposed in *Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment* (Hidalgo et al., 2026) for CIFAR10 and MNIST, which proposed the FedSaSync algorithm. Concretely, the results are exposed for both datasets in Figures 4 and 5, and in Tables 3 and 4
+
+**Datasets:** CIFAR10, MNIST
+
+**Hardware Setup:** These experiments were run on a desktop machine with an 12th Gen Intel(R) Core(TM) i7-12700 (20 CPU threads). Any machine with with 4 CPU cores or more would be able to run it in a reasonable amount of time. Note: the entire experiment runs on a CPU-only mode, but GPU support is included on code.
+
+**Contributors:** Víctor Hidalgo-Izquierdo, Carmen Carrión, Blanca Caminero
+
+
+## Experimental Setup
+
+**Task:** Image classification
+
+**Model:** A PyTorch simple CNN adapted from 'PyTorch: A 60 Minute Blitz'. This is the model used by default in Flower. Note: The model has been modified to adapt to each dataset input, as well as the lr (see `model.py`).
+
+**Dataset:** This baseline includes both CIFAR10 and MNIST datasets. They are partitioned into 10 clients following an IID partitioning where all clients receive data drawn from the same underlying distribution, ensuring balanced and homogeneous data across clients.
+
+| Dataset | # classes | # rounds | # partitions |     partitioning method     |  partition settings  |
+| :------ | :------: | :-------: | :----------: | :-------------------------: | :------------------: |
+| CIFAR10 |    10    |   50      |     10       |      IID Partitioning       |   Homogeneous data   |
+|  MNIST  |    10    |   25      |     10       |      IID Partitioning       |   Homogeneous data   |
+
+**Training Hyperparameters:** The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `flwr run .` directly)
+
+| Description             | Default Value                                      |
+| -------------------     | -------------------------------------------------- |
+| total clients           | 10                                                 |
+| clients per round       | 10                                                 |
+| client resources        | {'num_cpus': 2.0, 'num_gpus': 0.0}                 |
+| strategy name           | FedSaSync                                          |
+| number of rounds        | 50                                                 |
+| slow clients            | 0                                                  |
+| semiasynchronous degree | 10                                                 |
+| dataset name            | "uoft-cs/cifar10"                                  |
+| learning rate           | 0.01                                               |
+
+**Experiment configurations:** The following table shows the configurations to be used on the experiments, defined in `run_cifar10_experiments.sh` and `run_mnist_experiments.sh` (these configurations will later overwrite the default values with the `--run-config` option during `flwr run .`)
+| dataset name     | slow clients  | semiasynchronous degree | number of rounds                  | learning rate                     |
+| ---------------- | ------------- | ----------------------- | --------------------------------- | --------------------------------- |
+| {CIFAR10, MNIST} | {0, 1, 2}     | {7, 8, 9, 10, FedAvg}   | *fixed according to the experiment* | *fixed according to the experiment* |
+
+Note: `number of rounds` is 50 for CIFAR10, and 25 for MNIST; `learning rate` is 0.01 for CIFAR10, and 0.05 for MNIST
+
+## Environment Setup
+
+To construct the Python environment, simply run:
+
+```bash
+# Create the virtual environment
+pyenv virtualenv 3.12.12 FedSaSync
+
+# Activate it
+pyenv activate FedSaSync
+
+# Install the baseline
+pip install -e .
+```
+
+## Running the Experiments
+
+To run this FedSaSync, first ensure that your environment is properly activated as described above. For unique executions, do the following:
+
+```bash
+flwr run .  # this will run using the default settings in the `pyproject.toml`
+
+# you can override settings directly from the command line
+flwr run . --run-config "name='FedAvg' number-slow=1"   # for FedAvg with 1 slow client
+# for FedSaSync with 2 slow clients, semiasync degree 8, mnist dataset
+flwr run . --run-config "num-server-rounds=25 semiasync-deg=8 number-slow=2 dataset-name='ylecun/mnist'"    
+```
+
+The baseline includes the scripts `run_cifar10_experiments.sh` and `run_mnist_experiments.sh`, which are designed to execute the experiments reported in the paper using the predefined configurations. The configurations are described on the table below, at Experimental Setup:
+
+```bash
+bash run_cifar10_experiments.sh # CIFAR10
+bash run_mnist_experiments.sh   # MNIST
+```
+
+We include a python script to automatically print several graphs to summarise the executions (see `_static/graphing.py`). Depending on the experiments performed, change the global configuration to define what will be printed on the plots. All results are saved in `_static`. Each experiment generates two visualizations: a comparative plot grouped by the number of slow clients to analyze the impact of different semi-asynchronous degrees, and a summary table showing the mean training efficiency under each configuration, measured on loss per second. To generate these visualizations, proceed as follows:
+
+```bash
+python _static/graphing.py  # Plot the results after executing
+```
+
+Results for CIFAR10:
+
+![CIFAR10 loss over time comparison](_static/cifar10_comparison.svg)
+![CIFAR10 efficiency (loss/s)](_static/cifar10_efficiency.svg)
+
+Results for MNIST:
+
+![MNIST loss over time comparison](_static/mnist_comparison.svg)
+![MNIST efficiency (loss/s)](_static/mnist_efficiency.svg)