Self-managed distributed deployment

This tutorial outlines the steps for deploying the FEDn framework over a local network, using a single workstation or laptop as the host for the servier-side components, and other hosts or devices as clients. For general steps on how to run FEDn, see the quickstart tutorials.


For a secure and production-grade deployment solution over public networks, explore the FEDn Studio service at

Alternatively follow this tutorial substituting the hosts local IP with your public IP, open the neccesary ports (see which ports are used in docker-compose.yaml), and ensure you have taken additional neccesary security precautions.

Prerequisites - One host workstation and atleast one client device - Python 3.8, 3.9, 3.10 or 3.11 - Docker - Docker Compose

Launch a distributed FEDn Network

Start by noting your host’s local IP address, used within your network. Discover it by running ifconfig on UNIX or ipconfig on Windows, typically listed under inet for Unix and IPv4 for Windows.

Continue by following the standard procedure to initiate a FEDn network, for example using the provided docker-compose template. Once the network is active, upload your compute package and seed (for comprehensive details, see the quickstart tutorials).


This guide covers general local networks where server and client may be on different hosts but able to communicate on their private IPs. A common scenario is also to run fedn and the clients on localhost on a single machine. In that case, you can replace <host local ip> by “” below.

Configuring and Attaching Clients

On your client device, continue with initializing your client. To connect to the host machine we need to ensure we are routing the correct DNS to our hosts local IP address. We can do this using the standard FEDn client.yaml:

network_id: fedn-network
discover_host: api-server
discover_port: 8092

We can then run a client using docker by adding the hostname:ip mapping in the docker run command:

docker run \
-v $PWD/client.yaml:<client.yaml file location> \
<potentiel data pointers>
—add-host=api-server:<host local ip> \
—add-host=combiner:<host local ip> \
<image name> run client -in client.yaml --name client1

Alternatively updating the /etc/hosts file, appending the following lines for running naitively:

<host local ip>      api-server
<host local ip>      combiner

Start a training session

After connecting with your clients, you are ready to start training sessions from the host machine.