This repo contains the code and data for doing federated learning on MNIST dataset on Blockchain.
https://ieeexplore.ieee.org/document/8945913
Before you do anything else you will first need to install the following python packages:
absl-py==0.5.0
astor==0.7.1
certifi==2018.10.15
chardet==3.0.4
Click==7.0
cycler==0.10.0
Flask==1.0.2
gast==0.2.0
grpcio==1.15.0
h5py==2.8.0
idna==2.7
Jinja2==2.10
Keras-Applications==1.0.6
Keras-Preprocessing==1.0.5
kiwisolver==1.0.1
Markdown==3.0.1
MarkupSafe==1.0
matplotlib==3.0.0
numpy==1.15.2
protobuf==3.6.1
pyparsing==2.2.2
python-dateutil==2.7.3
requests==2.20.0
six==1.11.0
tensorboard==1.11.0
termcolor==1.1.0
urllib3==1.24
Werkzeug==0.14.1
ItsDangerous==1.1.0
tensorflow==1.11.0
These are specified in the src/packages_to_install.txt file.
This project was built using Python3 but may work with Python2 given a few minor tweaks.
The next step is to build the federated dataset to do federating learning on. You can prepare it by running this script:
python src/data/federated_data_extractor.py
The default split is 2 split_dataset(dataset,2) which can be changed as per your number of clients.
Once you've generated chunks of federated_data_x.d you can begin training. For this simply
run the following bash script:
./src/Run_BlockFL.sh
Assuming you've installed all dependencies and everything else successfully,
this should start federated learning on the generated federated datasets on blockchain.
Once you've finished training, you can get the aggregated globally updated model federated_modelx.block per round from the src/blocks folder.