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
Lightweight, open and easy to use:
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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