hybrid-approach-to-ppfl
hybrid-approach-to-ppfl copied to clipboard
Published
20 hours ago •
lainisourgod
lainisourgod
This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)
PoC Hybrid Approach
Project structure
-
main.py-- script for MNIST experiment (either Linear or CNN model). Defines dataset and dataloaders for trainer -
rnn_main.py-- script for RNN experiment -
trainer.py-- Trainer class that rules training and encryption stuff -
distro.py-- Party and Server classes that knows about model and about encryption and do it -
distro_paillier/-- sources of Threshold Paillier Homomorphic Encryption. this library is used. I've done minor changes todecryptfunction for it to work correctly with float numbers. -
config.py-- containes singleton config object used by all modules.
How to run
- Install system dependencies for crypto stuff:
sudo apt install libgmp-dev libmpfr-dev libmpc-dev. - Install python dependencies:
pip install -r requirements.txt. - Run MNIST experiment
python main.py. - Run RNN experiment
python rnn_main.py.
Metadata
20
Stars
4
Forks
Watchers
Owner
lainisourgod
Metadata
This is an implementation for paper "A Hybrid Approach to Privacy Preserving Federated Learning" (https://arxiv.org/pdf/1812.03224.pdf)