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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.)
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
Reddit
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.
Notes
Install the libraries listed in requirements.txt
I.e. with pip: run pip3 install -r requirements.txt
Go to directory of respective dataset for instructions on generating data
in MacOS check if wget is installed and working
models directory contains instructions on running baseline reference implementations
