This repository implements common ways of splitting a dataset to create non-i.i.d. federated learning distributions.
- iid: distributed i.i.d. between clients with equal sample sizes
- shard: based on the strategy outlined in Communication-Efficient Learning of Deep Networks from Decentralized Data (McMahan et. al.). Generalized to let the user specify the number of shard received by each client
- Dirichlet equal: Uses a Dirichlet distribution parameterized by alpha [0.01, infinity) and partitions samples so that clients have an equal number of samples
- Dirichlet unequal: Uses a Dirichlet distribution parameterized by alpha [0.01, infinity) and partitions samples so that clients have an unequal number of samples (Not implemented yet)
Sample bar graphs for the proportions of CIFAR10 classes of 3 randomly selected clients, parameterized by alpha=0.1. L1, L2, ..., L10 along the y-axis are the class labels, the x-axis are the proportions of each class s.t. L1 + L2 + ... + L10 = 1.0
| Client No. | Proportion | Visualization |
|---|---|---|
| 10 | [0. 0. 0.288 0. 0. 0. 0. 0. 0.712 0. ] |  |
| 4 | [0. 0.0028 0.1088 0. 0. 0.5692 0.3172 0. 0.002 0. ] |  |
| 5 | [0. 0.3848 0.0084 0.0056 0. 0.1204 0.3592 0.1192 0.0024 0. ] |  |
Install requirements and execute python main.py. You can set the type of split, number of users and alpha for the
Dirichlet distribution in main.py