LEAF is a benchmarking framework for learning in federated settings. This repository provides a custom PyTorch dataset class for the LEAF femnist dataset

the file femnist.tar.gz contains

• femnist_train.pt
• femnist_test.pt
• femnist_user_keys.pt

Contains a list of individual authors of the letters and digits in the dataset

These files contain the entire femnist dataset split into 80% train, 20% test partitions. Each sample is associated with one of the authors listed in femnist_user_keys.pt so that samples may be divided between federated clients by author if required. The data was obtained from the TalwalkarLab/leaf repository by following the setup instructions provided and executing the command ./preprocess.sh -s niid --sf 1.0 -k 0 -t sample to obtain the full sized dataset.

** Note: the dataset provided here contains 817851 samples, not 805263 samples as specified here.

See TalwalkarLab/leaf#49 (not resolved at this time 2022-07-06)

femnist_dataset.py is modeled after The torchvision MNIST Class and will work similarly with PyTorch Dataloaders.

Parameters:

• root: the path to the root directory where the data will be stored

• train: set True for training data and False for test data

• transform: PyTorch image transformations

• target_transform: label transformations

• download: set True to download the femnist dataset, False otherwise

Example: train = FEMNIST(root='./data', train=True, transform=None, target_transform=None, download=True)