MNIST-Federated-Learning

Please find below the descriptions of the three configurations for partitioning the MNIST Train dataset into 10 clients and the MNIST Train data: 
 

1. Balanced Distribution: In the first configuration, the MNIST dataset is partitioned among 10 clients in a balanced manner. This means that the data samples from each class are evenly distributed among the clients. Each client receives a roughly equal number of images from each digit class, ensuring that the distribution of samples across clients is proportional and representative of the overall dataset.    [ Config 1]
2. Heterogeneous Distribution (One Class per Client): In the second configuration, the MNIST dataset is partitioned in a heterogeneous manner, where each client is assigned a single digit class exclusively. This means that one client will only receive images of the digit '0', another client will receive images of the digit '1', and so on. In this setup, each client becomes an expert in classifying a specific digit, allowing for specialized training and evaluation. [ Config 2]
3. Mixed Distribution: In the third configuration, the MNIST dataset is partitioned using a mixed distribution approach. This means that the data samples from all digit classes are distributed among the 10 clients, but the distribution is not necessarily balanced. The number of samples assigned to each client may vary for different digit classes, resulting in an uneven distribution across the clients. This configuration aims to capture both the overall diversity of the dataset and the varying difficulty levels of classifying different digits. [ Config 3 ]

Mnist-dataset/
├── config1/
│   ├── client-1/
│   │   └── data.csv
│   ├── client-2/
│   │   └── data.csv
│   ├── client-3/
│   │   └── data.csv
│   └── ...
├── config2/
│   ├── client-1/
│   │   └── data.csv
│   ├── client-2/
│   │   └── data.csv
│   ├── client-3/
│   │   └── data.csv
│   └── ...
├── config3/
│   ├── client-1/
│   │   └── data.csv
│   ├── client-2/
│   │   └── data.csv
│   ├── client-3/
│   │   └── data.csv
│   └── ...
└── mnist_test.csv
 

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License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

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