donifarakan 0.1.3

Donifarakan is a federated learning framework designed specially for the finance sector (banks, fintech companies, etc.), where the stakeholders will train a generalized model on their local data without sharing them in order to make predictions, prevent market risks, assess news impacts on the stock market, and more.

Introduction

"Dɔni farakan" is a robust federated learning framework designed to enable distributed machine/deep learning while safeguarding data privacy. This framework enable multiple clients to collaboratively train sophisticated models without exposing their raw data. Instead, clients share model updates (e.g., weights) with a central server. The server aggregates these updates to create a generalized and high-performing model.

Specifically tailored for the finance sector (banks, fintech companies, etc.), Dɔnifarakan allows stakeholders to train models on their local data without compromising sensitive information. This collaborative approach facilitates a wide range of applications, including:

• Making accurate predictions (on stock trends,...)
• Preventing market risks.
• Assessing the impact of news on stock market.
• ....

Follow the steps below to understand how does it works and start creating your own plateform where companies might susbcribe as client in order to use it!

Requirements

Before to start you need to have python installed on your laptop. If you don't have python please follow the steps on their website in order to get it.

Getting started

As we are in the context of federated learning, you should have at least two computers. One representing the central server and another one representing a sample client. You can many clients and as many as you can. Make sure that the computers including central and clients are all on the same local network.

On your central server computer

1. Create a new folder for your project. You can name it " MyFramework "
2. Go the folder directory in your terminal or cmd. $cd "MyFramework"
3. Install the donifarakan framework. $ pip install donifarakan
4. Create a python file which will be used to launch your central server. $ touch server.py
5. Open the server.py file then copy and paste this code below:

# Here we are loading the required methods from donifrakan
from donifarakan.server import start

# Here we launching the central server
start(ip_address="10.12.167.82",port=6590)

On your client device

1. Create a new folder for your project. You can name it " MyCompany AI "
2. Go the folder directory in your terminal or cmd. $cd "MyCompany AI"
3. Install the donifarakan framework. pip install donifarakan
4. Create a python file which will be used to train a model on the client local data. $touch train.py
5. Open the train.py file then copy and paste this code below:

# Here we are loading the required methods from donifrakan
from donifarakan.utils import download_dataset
from donifarakan.client import global_model, train

# If you don't have any dataset yet, execute this method to download a sample dataset, by default it will download a stock price dataset if you don't specify any!
download_dataset()

# Here you will be requested to provide your central server details in order to get the global model
get_global_model()

# Here you will be requested to provide all the details about your dataset before to start the training
train()

Contribution

This project is an open source, to contribute on it fell free to explore the architecture and leave your comments and suggestions!

FAQ

For any query you can reach me out at [e-mail]

LICENCE

MIT License

Copyright (c) 2025 TRAORE ADAMA SEYDOU