- Homepage: leaf.cmu.edu
- Paper: "LEAF: A Benchmark for Federated Settings"
- FEMNIST
- Overview: Image Dataset
- Details: 62 different classes (10 digits, 26 lowercase, 26 uppercase), images are 28 by 28 pixels (with option to make them all 128 by 128 pixels), 3500 users
- Task: Image Classification
- Sentiment140
- Overview: Text Dataset of Tweets
- Details 660120 users
- Task: Sentiment Analysis
- Shakespeare
- Overview: Text Dataset of Shakespeare Dialogues
- Details: 1129 users (reduced to 660 with our choice of sequence length. See bug.)
- Task: Next-Character Prediction
- Celeba
- Overview: Image Dataset based on the Large-scale CelebFaces Attributes Dataset
- Details: 9343 users (we exclude celebrities with less than 5 images)
- Task: Image Classification (Smiling vs. Not smiling)
- Synthetic Dataset
- Overview: We propose a process to generate synthetic, challenging federated datasets. The high-level goal is to create devices whose true models are device-dependant. To see a description of the whole generative process, please refer to the paper
- Details: The user can customize the number of devices, the number of classes and the number of dimensions, among others
- Task: Classification
- Overview: We preprocess the Reddit data released by pushshift.io corresponding to December 2017.
- Details: 1,660,820 users with a total of 56,587,343 comments.
- Task: Next-word Prediction.
- Install the libraries listed in
requirements.txt- I.e. with pip: run
pip3 install -r requirements.txt
- I.e. with pip: run
- Go to directory of respective dataset for instructions on generating data
- in MacOS check if
wgetis installed and working
- in MacOS check if
modelsdirectory contains instructions on running baseline reference implementations
