FLlib

FLlib is a powerful framework that makes it simple to implement the federated learning you want. The framework uses TensorFlow used widely for model processing, and low-level communication implemented in C/C++ for federated learning weights.

※ C/C++ build tools and Tensorflow environment are required to use FLlib framework.

build

Build the shared libraries required by FLlib through make.

$> make

Communication libraries, module/libc_server.so and module/libc_client.so are created if the build succeeds In an environment where build is not possible, you can download and use module/libc_server.so and module/libc_client.so in the Release package, but normal operation cannot be guaranteed.

example

FLlib framework consists of files in module folder and FLlib.py. client.py and server.py are example codes.

The example server can be run with the command below.

$> python3 server.py

The example client can be run with the command below.

$> python3 client.py

usage

Federated learning server and client can be implemented by inheriting classes from FLlib.

Child < Parent

MNISTModel (in server.py) < FLModel
MNISTModel (in client.py) < FLModel
MNISTServer < FLServer
MNISTClient < FLClient

You can configure the federated learning you want by inheriting the class in FLlib and modifying the inherited function appropriately.