> For the complete documentation index, see [llms.txt](https://medomicslab.gitbook.io/medfl-app-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://medomicslab.gitbook.io/medfl-app-docs/medfl-review/medfl-package-review/simulation.md).
# Simulation
### Overview
The **Simulation Mode** of MEDfl allows federated learning experiments to be executed within a single controlled environment.
In this mode:
* No physical client machines are required
* No real network orchestration is needed
* Multiple clients are programmatically simulated
* All components run within the same runtime context
Client behavior is emulated internally, enabling reproducible and controlled federated learning experiments.
### Purpose of This Review
The objective of this review is to validate the correctness, stability, and usability of the MEDfl Python package when operating in simulation mode.
Specifically, this review aims to:
* Verify that simulation pipelines can be configured and executed end-to-end
* Ensure datasets are correctly partitioned across simulated clients
* Validate client initialization, local training, aggregation, and evaluation steps
* Confirm that metrics, logs, and artifacts are generated consistently
* Assess robustness under different configuration settings
{% hint style="info" %}
This review does **not** aim to benchmark model performance, but rather to ensure that the simulation mode behaves as expected and produces reliable outputs suitable for further experimentation or real-world deployment.
{% endhint %}
### Documentation
The following resources describe how simulation mode is implemented and used in MEDfl.
### Getting Started with the Tutorial
To start testing MEDfl using this tutorial, simply visit this [**GitHub repository** ](https://github.com/ouaelesi/MEDfl-package-review)and download the **tutorial notebook along with the provided datasets**.
The GitHub repository used for this review is organized into four main folders:
`/models :` Contains the pretrained model used for transfer learning experiments.\
This model is loaded during training to initialize the federated learning process when transfer learning is enabled.
`/data :` Contains the dataset files already partitioned across multiple clients.\
Each file represents the local dataset of a specific client, simulating decentralized data ownership.
`/simulation :` Contains the Jupyter notebook used for the **Simulation Mode review**.\
This notebook runs the full federated learning workflow in a single controlled environment.
`/rw :` Contains the notebooks used for the **Real-World Mode review**.\
These notebooks are intended to be executed separately by the server and each client on different machines.
Once downloaded, you can run the notebook inside `/simulation` locally and follow the tutorial to reproduce the results and experiment with different configurations.
---
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