Skip to content
Leaf: A Benchmark for Federated Settings
Python Jupyter Notebook Shell
Branch: master
Clone or download
Latest commit 15681b5 Sep 18, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data synthetic data Sep 17, 2019
docs synthetic data Sep 17, 2019
models synthetic data Sep 17, 2019
paper_experiments removes finetuning functionality Jun 3, 2019
.gitignore Added writeups, contact, links Mar 30, 2019
.nojekyll Added .nojekyll file Apr 1, 2019
LICENSE.md Adds two-clause BSD license. Dec 14, 2018
README.md synthetic data Sep 17, 2019
requirements.txt adds gitignore and requirements Oct 26, 2018

README.md

Leaf: A Benchmark for Federated Settings

Resources

Datasets

  1. 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
  1. Sentiment140
  • Overview: Text Dataset of Tweets
  • Details 660120 users
  • Task: Sentiment Analysis
  1. Shakespeare
  • Overview: Text Dataset of Shakespeare Dialogues
  • Details: 2288 users
  • Task: Next-Character Prediction
  1. Celeba
  1. 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

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
  • models directory contains instructions on running baseline reference implementations
You can’t perform that action at this time.