Auto-weighted Robust Federated Learning with Corrupted Data Sources

This repository contains the code and experiments for the paper:

Auto-weighted Robust Federated Learning with Corrupted Data Sources

Datasets

1. CIFAR-10

• Overview: Image Dataset. See CIFAR-10
• Details: 10 different classes, images are 32 by 32 pixels.
• Task: Image Classification

2. 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

3. Shakespeare

• Overview: Text Dataset of Shakespeare Dialogues
• Details: 1129 users (reduced to 660 with our choice of sequence length.
• Task: Next-Character Prediction

Notes

• Install the libraries listed in requirements.txt
  • I.e. with pip: run pip3 install -r requirements.txt
  • To prepare the dataset for the paper, run sudo configure.sh
• Go to directory of respective dataset for instructions on generating data
• models directory contains instructions on running baseline reference implementations

Ref

LEAF benchmark

• Homepage: leaf.cmu.edu
• Paper: "LEAF: A Benchmark for Federated Settings"