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https://github.com/Xtra-Computing/FedTree
A tree-based federated learning system (MLSys 2023)
https://github.com/Xtra-Computing/FedTree
distributed federated-learning gradient-boosting-decision-trees
Last synced: 3 months ago
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A tree-based federated learning system (MLSys 2023)
- Host: GitHub
- URL: https://github.com/Xtra-Computing/FedTree
- Owner: Xtra-Computing
- License: apache-2.0
- Created: 2020-10-13T12:29:12.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-10-04T02:10:18.000Z (about 1 year ago)
- Last Synced: 2024-07-15T10:46:44.182Z (4 months ago)
- Topics: distributed, federated-learning, gradient-boosting-decision-trees
- Language: C++
- Homepage: https://fedtree.readthedocs.io/en/latest/index.html
- Size: 35.7 MB
- Stars: 139
- Watchers: 7
- Forks: 38
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[Documentation](https://fedtree.readthedocs.io/en/latest/index.html)
# Overview
**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.
# Getting Started
You can refer to our primary documentation [here](https://fedtree.readthedocs.io/en/latest/index.html).
## Prerequisites
* [CMake](https://cmake.org/) 3.15 or above
* [GMP](https://gmplib.org/)
* [NTL](https://libntl.org/)
* [gRPC](https://grpc.io/docs/languages/cpp/quickstart/) 1.50.0 (required for distributed version)
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
```
## Clone and Install submodules
```
git clone https://github.com/Xtra-Computing/FedTree.git
cd FedTree
git submodule init
git submodule update
```
# Standalone Simulation
## Build on Linux (Recommended)
```bash
# under the directory of FedTree
mkdir build && cd build
cmake .. -DDISTRIBUTED=OFF
make -j
```
## Build on MacOS
### Build with Apple Clang
You need to install ```libomp``` for MacOS.
```
brew install libomp
```
Install FedTree:
```bash
# 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
```bash
# under 'FedTree' directory
./build/bin/FedTree-train ./examples/vertical_example.conf
```
# Distributed Setting
For each machine that participates in FL, it needs to build the library first. When building the library, passing `-DDISTRIBUTED=ON` option to cmake.
```bash
mkdir build && cd build
cmake .. -DDISTRIBUTED=ON
make -j
```
Then, 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.
[//]: # (export CPLUS_INCLUDE_PATH=./build/_deps/grpc-src/include/:$CPLUS_INCLUDE_PATH)
[//]: # (export CPLUS_INCLUDE_PATH=./build/_deps/grpc-src/third_party/protobuf/src/:$CPLUS_INCLUDE_PATH)
## Distributed Horizontal FedTree
```bash
# 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
```
## Distributed Vertical FedTree
```bash
# 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 1
```
# Other information
FedTree is built based on [ThunderGBM](https://github.com/Xtra-Computing/thundergbm), which is a fast GBDTs and Radom Forests training system on GPUs.
# Citation
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}
}
```
# Call for contributions
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].