XFL is a high-performance, high-flexibility, high-applicability, lightweight, open and easy-to-use Federated Learning framework. It supports a variety of federation models in both horizontal and vertical federation scenarios. To enable users to jointly train model legally and compliantly to unearth the value of their data, XFL adopts homomorphic encryption, differential privacy, secure multi-party computation and other security technologies to protect users' local data from leakage, and applies secure communication protocols to ensure communication security.
High-performance algorithm library
Flexible deployment
Lightweight, open and easy to use:
Support for large language models
Running in standalone mode
# create and activate the virtual environment
conda create -n xfl python=3.9.7
conda activate xfl
# install redis and other dependencies
# Ubuntu
apt install redis-server
# CentOS
yum install epel-release
yum install redis
# MacOS
brew install redis
brew install coreutils
# install python dependencies
# update pip
pip install -U pip
# install dependencies
pip install -r requirements.txt
# set permission
sudo chmod 755 /opt
# enter the project directory
cd ./demo/vertical/logistic_regression/2party
# start running the demo
sh run.sh
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