FedPS is a Python module designed for data preprocessing in Federated Learning, primarily leveraging data sketching techniques.
- Python (>= 3.8)
- Scikit-learn (>= 1.3)
- NumPy (>= 1.20)
- DataSketches
- PyZMQ
- Create a Python env
conda create --name fedps python=3.9
conda activate fedps- Clone this project
git clone https://github.com/primihub/fedps.git- Build the project
cd fedps
pip install .# Run in three terminals
python example/client1.py
python example/client2.py
python example/server.py- Set up communication channels
# Client1 channel
from fedps.channel import ClientChannel
channel = ClientChannel(
local_ip="127.0.0.1", local_port=5556,
remote_ip="127.0.0.1", remote_port=5555,
)# Client2 channel
from fedps.channel import ClientChannel
channel = ClientChannel(
local_ip="127.0.0.1", local_port=5557,
remote_ip="127.0.0.1", remote_port=5555,
)# Server channel
from fedps.channel import ServerChannel
channel = ServerChannel(
local_ip="127.0.0.1", local_port=5555,
remote_ip=["127.0.0.1", "127.0.0.1"],
remote_port=[5556, 5557],
)- Specify
FL_typeandrolein the preprocessor
-
FL_type: "H" (Horizontal) or "V" (Vertical) federated learning -
role: "client" or "server"
# Client1 code example
from fedps.preprocessing import MinMaxScaler
X = [[-1, 2], [-0.5, 6]]
est = MinMaxScaler(FL_type="H", role="client", channel=channel)
Xt = est.fit_transform(X)
print(Xt)# Client2 code example
from fedps.preprocessing import MinMaxScaler
X = [[0, 10], [1, 18]]
est = MinMaxScaler(FL_type="H", role="client", channel=channel)
Xt = est.fit_transform(X)
print(Xt)# Server code example
from fedps.preprocessing import MinMaxScaler
est = MinMaxScaler(FL_type="H", role="server", channel=channel)
est.fit()- Run the script
-
Discretization
-
Encoding
-
Scaling
-
Transformation
-
Imputation