Documentation

Overview

FedTree is a federated learning system for tree-based models. It is designed to be highly efficient, effective, and secure. It is under development and has the following features currently.

• Federated training of gradient boosting decision trees.
• Parallel computing on multi-core CPUs.
• Supporting homomorphic encryption and differential privacy.
• Supporting classification and regression.

The overall architecture of FedTree is shown below.

Getting Started

You can refer to our primary documentation here.

Prerequisites

• CMake 3.15 or above
• NTL library

You can follow the following commands to install NTL library.

wget https://libntl.org/ntl-11.4.4.tar.gz
tar -xvf ntl-11.4.4.tar.gz
cd ntl-11.4.4/src
./configure
make
make check
sudo make install

If you install the NTL library at another location, please also modify the CMakeList files of FedTree accordingly (line 64 of CMakeLists.txt).

Install submodules

git submodule init src/test/googletest
git submodule init thrust
git submodule update

Build on Linux

# under the directory of FedTree
mkdir build && cd build 
cmake ..
make -j

Build on MacOS

Build with Apple Clang

You need to install libomp for MacOS.

brew install libomp

Install FedTree:

# under the directory of FedTree
mkdir build
cd build
cmake -DOpenMP_C_FLAGS="-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" \
  -DOpenMP_C_LIB_NAMES=omp \
  -DOpenMP_CXX_FLAGS="-Xpreprocessor -fopenmp -I/usr/local/opt/libomp/include" \
  -DOpenMP_CXX_LIB_NAMES=omp \
  -DOpenMP_omp_LIBRARY=/usr/local/opt/libomp/lib/libomp.dylib \
  ..
make -j

Run training

# under 'FedTree' directory
./build/bin/FedTree-train ./examples/vertical_example.conf

Features in development

The following features are in development.

• Distributed Computing.
• Training on GPUs.
• Federated Training of Random Forests.
• Python interfaces.

Other information

FedTree is built based on ThunderGBM, which is a fast GBDTs and Radom Forests training system on GPUs.