Welcome to the official repository housing the FEDHE-Graph implementation for our solution Continuum! This repository provides you with the necessary tools and resources to leverage federated learning techniques within the context of Continuum, a comprehensive framework for federated learning research.
The command are used in an environnement that consist of Windows 11 with anaconda installed
First create the conda environnement for fedml with MPI support
conda create --name fedml-pip python=3.8
conda activate fedml-pip
conda install --name fedml-pip pip
conda install -c conda-forge mpi4py openmpi
pip install "fedml[MPI]"
Clone the Continuum FedML project onto your current folder
git clone https://github.com/kamelferrahi/Continuum_FL
Install the necessary packages for Continuum to run
conda install -c conda-forge aiohttp=3.9.1 aiosignal=1.3.1 anyio=4.2.0 attrdict=2.0.1 attrs=23.2.0 blis=0.7.11 boto3=1.34.12 botocore=1.34.12 brotli=1.1.0 catalogue=2.0.10 certifi=2023.11.17 chardet=5.2.0 charset-normalizer=3.3.2 click=8.1.7 cloudpathlib=0.16.0 confection=0.1.4 contourpy=1.2.0 cycler=0.12.1 cymem=2.0.8 dgl=1.1.3 dill=0.3.7 fastapi=0.92.0 fedml=0.8.13.post2 filelock=3.13.1 fonttools=4.47.0 frozenlist=1.4.1 fsspec=2023.12.2 gensim=4.3.2 gevent=23.9.1 geventhttpclient=2.0.9 gitdb=4.0.11 GitPython=3.1.40 GPUtil=1.4.0 graphviz=0.8.4 greenlet=3.0.3 h11=0.14.0 h5py=3.10.0 httpcore=1.0.2 httpx=0.26.0 idna=3.6 Jinja2=3.1.2 jmespath=1.0.1 joblib=1.3.2 kiwisolver=1.4.5 langcodes=3.3.0 MarkupSafe=2.1.3 matplotlib=3.8.2 mpi4py=3.1.3 mpmath=1.3.0 multidict=6.0.4 multiprocess=0.70.15 murmurhash=1.0.10 networkx=2.8.8 ntplib=0.4.0 numpy=1.26.3 nvidia-cublas-cu12=12.1.3.1 nvidia-cuda-cupti-cu12=12.1.105 nvidia-cuda-nvrtc-cu12=12.1.105 nvidia-cuda-runtime-cu12=12.1.105 nvidia-cudnn-cu12=8.9.2.26 nvidia-cufft-cu12=11.0.2.54 nvidia-curand-cu12=10.3.2.106 nvidia-cusolver-cu12=11.4.5.107 nvidia-cusparse-cu12=12.1.0.106 nvidia-nccl-cu12=2.18.1 nvidia-nvjitlink-cu12=12.3.101 nvidia-nvtx-cu12=12.1.105 onnx=1.15.0 packaging=23.2 paho-mqtt=1.6.1 pandas=2.1.4 pathtools=0.1.2 pillow=10.2.0 preshed=3.0.9 prettytable=3.9.0 promise=2.3 protobuf=3.20.3 psutil=5.9.7 py-machineid=0.4.6 pydantic=1.10.13 pyparsing=3.1.1 python-dateutil=2.8.2 python-rapidjson=1.14 pytz=2023.3.post1 PyYAML=6.0.1 redis=5.0.1 requests=2.31.0 s3transfer=0.10.0 scikit-learn=1.3.2 scipy=1.11.4 sentry-sdk=1.39.1 setproctitle=1.3.3 shortuuid=1.0.11 six=1.16.0 smart-open=6.3.0 smmap=5.0.1 sniffio=1.3.0 spacy=3.7.2 spacy-legacy=3.0.12 spacy-loggers=1.0.5 SQLAlchemy=2.0.25 srsly=2.4.8 starlette=0.25.0 sympy=1.12 thinc=8.2.2 threadpoolctl=3.2.0 torch=2.1.2 torch-cluster=1.6.3 torch-scatter=2.1.2 torch-sparse=0.6.18 torch-spline-conv=1.2.2 torch_geometric=2.4.0 torchvision=0.16.2 tqdm=4.66.1 triton=2.1.0 tritonclient=2.41.0 typer=0.9.0 typing_extensions=4.9.0 tzdata=2023.4 tzlocal=5.2 urllib3=2.0.7 uvicorn=0.25.0 wandb=0.13.2 wasabi=1.1.2 wcwidth=0.2.12 weasel=0.3.4 websocket-client=1.7.0 wget=3.2 yarl=1.9.4 zope.event=5.0 zope.interface=6.1
Finally run the federated algorithm using the mpi command
hostname > mpi_host_file
mpirun -np 4 -hostfile mpi_host_file --oversubscribe python main.py --cf fedml_config.yaml
You can adjust federated learning parameters in the fedml_config.yaml file.
Parameters such as aggregation algorithm, number of clients, and clients per round for aggregation can be modified:
train_args:
federated_optimizer: "FedAvg"
client_id_list:
client_num_in_total: 4
client_num_per_round: 4
The algorithm tested are FedAvg, FedProx and FedOpt
The experiments utilize datasets similar to those in the original Continuum project. To change datasets, edit the fedml_config.yaml file:
data_args:
dataset: "wget"
data_cache_dir: ~/fedgraphnn_data/
part_file: ~/fedgraphnn_data/partition
Feel free to explore and modify these settings according to your specific requirements!