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RFA: Robust Aggregation for Federated Learning

Please see here for a PyTorch implementation of RFA.

Please see here for a self-contained implementation of the geometric median.

This code provides an implementation of robust aggregation algorithms for federated learning. This codebase is based on a fork of the Leaf benchmark suite and provides scripts to reproduce the experimental results in the paper Robust Aggregation for Federated Learning.

If you use this code, please cite the paper using the bibtex reference below

@article{pillutla2022robust,
  author={Pillutla, Krishna and Kakade, Sham M. and Harchaoui, Zaid},
  journal={IEEE Transactions on Signal Processing}, 
  title={{Robust Aggregation for Federated Learning}}, 
  year={2022},
  volume={70},
  number={},
  pages={1142-1154},
  doi={10.1109/TSP.2022.3153135}
}

Introduction

Federated Learning is a paradigm to train centralized machine learning models on data distributed over a large number of devices such as mobile phones. A typical federated learning algorithm consists in local computation on some of the devices followed by secure aggregation of individual device updates to update the central model.

The accompanying paper describes a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending outlier updates to the server.

This code compares the RobustFedAgg algorithm proposed in the accompanying paper to the FedAvg algorithm (McMahan et. al. 2017) as well as stochastic gradient descent. The code has been developed from a fork of Leaf, commit 51ab702af932090b3bd122af1a812ea4da6d8740.

Installation

This code is written in Python 3.6 and has been tested on Tensorflow 1.12. A conda environment file is provided in rfa.yml with all dependencies except Tensorflow. It can be installed by using conda as follows

conda env create -f rfa.yml 

Installing Tensorflow: Instructions to install a version of Tensorflow compatible with the CUDA on your hardware can be found here. Versions of Tensorflow compatible with a given CUDA version are given here.

The primary dependencies are Tensorflow, Numpy, Scipy, Pillow and Pandas. The code has been tested on Ubuntu 16.04.

Data Setup

1. EMNIST (Called FEMNIST here)

• Overview: Character Recognition Dataset
• Details: 62 classes (10 digits, 26 lowercase, 26 uppercase), 3500 total users, 1000 users used for experiments
• Task: Image Classification
• Setup: Go to data/femnist and run the command (takes ~1 hour and ~25G of disk space)

time ./preprocess.sh -s niid --sf 1.0 -k 100 -t sample

2. Shakespeare

• Overview: Text Dataset of Shakespeare Dialogues
• Details: 2288 total users, 628 users used for experiments
• Task: Next-Character Prediction
• Setup: Go to data/shakespeare and run the command (takes ~17 sec and ~50M of disk space)

time ./preprocess.sh -s niid --sf 1.0 -k 100 -t sample -tf 0.8

Reproducing Experiments in the Paper

Once the data has been set up, the scripts provided in the folder experiments/ can be used to reproduce the experiments in the paper. Note that GPU computations are non-deterministic. Consequently, the ConvNet and LSTM experiments reported in the paper, which were run using GPUs, are not perfectly reproducible.

From the base folder of this repository, first create the folder outputs as

mkdir outputs

and run the scripts, for example, shakespeare_lstm.sh as

./experiments/main/shakespeare_lstm.sh