FedTree is a federated learning system for tree-based models. It is designed to be highly efficient, effective, and secure. It has the following features currently.
- Federated training of gradient boosting decision trees.
- Parallel computing on multi-core CPUs and GPUs.
- Supporting homomorphic encryption, secure aggregation and differential privacy.
- Supporting classification and regression.
The overall architecture of FedTree is shown below.
You can refer to our primary documentation here.
You can follow the following commands to install NTL library.
wget https://libntl.org/ntl-11.5.1.tar.gz
tar -xvf ntl-11.5.1.tar.gz
cd ntl-11.5.1/src
./configure SHARED=on
make
make check
sudo make install
If you install the NTL library at another location, please pass the location to the NTL_PATH when building the library (e.g., cmake .. -DNTL_PATH="PATH_TO_NTL").
For gRPC, please remember to add the local bin folder to your path variable after installation, e.g.,
export PATH="$gRPC_INSTALL_DIR/bin:$PATH"
We suggest you install gPRC 1.50.0, i.e., using -b v1.50.0 when cloning gRPC repo.
If your gRPC version is not 1.50.0, you need to go to src/FedTree/grpc directory and run
protoc -I ./ --grpc_out=. --plugin=protoc-gen-grpc=`which grpc_cpp_plugin` ./fedtree.proto
protoc -I ./ --cpp_out=. ./fedtree.proto
git clone https://github.com/Xtra-Computing/FedTree.git
cd FedTree
git submodule init
git submodule update
# under the directory of FedTree
mkdir build && cd build
cmake .. -DDISTRIBUTED=OFF
make -jYou 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# under 'FedTree' directory
./build/bin/FedTree-train ./examples/vertical_example.confFor each machine that participates in FL, it needs to build the library first. When building the library, passing -DDISTRIBUTED=ON option to cmake.
mkdir build && cd build
cmake .. -DDISTRIBUTED=ON
make -jThen, write your configuration file where you should specify the ip address of the server machine (ip_address=xxx). Run FedTree-distributed-server in the server machine and run FedTree-distributed-party in the party machines.
Here are two examples for horizontal FedTree and vertical FedTree.
# under 'FedTree' directory
# under server machine
./build/bin/FedTree-distributed-server ./examples/adult/a9a_horizontal_server.conf
# under party machine 0
./build/bin/FedTree-distributed-party ./examples/adult/a9a_horizontal_p0.conf 0
# under party machine 1
./build/bin/FedTree-distributed-party ./examples/adult/a9a_horizontal_p1.conf 1# under 'FedTree' directory
# under server (i.e., the party with label) machine 0
./build/bin/FedTree-distributed-server ./examples/credit/credit_vertical_p0_withlabel.conf
# open a new terminal
./build/bin/FedTree-distributed-party ./examples/credit/credit_vertical_p0_withlabel.conf 0
# Under party machine 1
./build/bin/FedTree-distributed-party ./examples/credit/credit_vertical_p1.conf 1FedTree is built based on ThunderGBM, which is a fast GBDTs and Radom Forests training system on GPUs.
Please cite our paper if you use FedTree in your work.
@inproceedings{fedtree,
title={FedTree: A Federated Learning System For Trees},
author={Li, Qinbin and Wu, Zhaomin and Cai, Yanzheng and Han, Yuxuan and Yung, Ching Man and Fu, Tianyuan and He, Bingsheng},
booktitle={Proceedings of Machine Learning and Systems},
year={2023}
}
Our goal is to make FedTree stronger and we're glad if you can contribute to FedTree. If you'd like to contribute to FedTree in-depth and are familiar with C++, kindly send your CV to [email protected].
