Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
ML
 
 
 
 
 
 
 
 
 
 

FoolsGold

A sybil-resilient distributed learning protocol that penalizes Sybils based on their gradient similarity.

FoolsGold is also described in two papers:

  1. Peer-reviewed conference paper pdf:
"The Limitations of Federated Learning in Sybil Settings." 
Clement Fung, Chris J.M. Yoon, Ivan Beschastnikh.
To appear in 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID) 2020.

Bibtex:

@InProceedings{Fung2020,
  title     = {{The Limitations of Federated Learning in Sybil Settings}},
  author    = {Clement Fung and Chris J. M. Yoon and Ivan Beschastnikh},
  year      = {2020},
  series    = {RAID},
  booktitle = {Symposium on Research in Attacks, Intrusion, and Defenses},
}
  1. Arxiv paper

Running a minimal MNIST example

Get the MNIST data.

Download and gunzip files from http://yann.lecun.com/exdb/mnist/
Move all the outputted files to ML/data/mnist. Navigate to that directory: cd ML/data/mnist and run parse_mnist.py

Create poisoned MNIST 1-7 data

From main directory navigate to the ML directory: cd ML/
Run: python code/misslabel_dataset.py mnist 1 7

Run FoolsGold

From main directory navigate to the ML directory: cd ML/
And run the following command for a 5 sybil, 1-7 attack on mnist.

python code/ML_main.py mnist 1000 5_1_7

About

A sybil-resilient distributed learning protocol.

Resources

Releases

No releases published

Packages

No packages published

Contributors 4

  •  
  •  
  •  
  •