#federated-learning #fl #ai #machine-learning


The Xayn Network project is building a privacy layer for machine learning so that AI projects can meet compliance such as GDPR and CCPA. The approach relies on Federated Learning as enabling technology that allows production AI applications to be fully privacy compliant.

2 unstable releases

0.10.0 Sep 22, 2020
0.9.0 Jul 24, 2020

#24 in Machine learning

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5.5K SLoC

XayNet: federated learning made private, performant, and ubiquitous

tags: Xayn, Federated Learning, Privacy

This is the main source code repository for xaynet.

Developers: feel free to jump to the technical "Getting Started" section.

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Want a framework that supports federated learning on the edge, in desktop browsers, integrates well with mobile apps, is performant, and preserves privacy? Welcome to XayNet, written entirely in Rust!

Making federated learning easy for developers

Frameworks for machine learning - including those expressly for federated learning - exist already. These frameworks typically require the use of specific machine learning technology - for example TensorFlow - or facilitate federated learning of cross-silo use cases - for example in collaborative learning across a limited number of hospitals.

We want to give developers more freedom of choice and abilities in the creation of federated learning software. By doing this, we hope to also increase the pace and scope of adoption of federated learning in practice.

Concretely, we provide developers with:

  • My AI tools: The flexibility to use the machine-learning frameworks and tools of their choice.
  • My app dev tools: The ability to integrate federated learning into apps written in Dart, Python or other languages of choice, as well as frameworks like Flutter.
  • "Federated learning" everywhere: The ability to run federated learning everywhere - be it desktop browsers, smartphones or micro-controllers.
  • "Federated learning" inside: A simple integration means of making an AI application ready for federated learning.
  • Privacy by design: A communication protocol for federated learning that scales, is secure, and preserves the privacy of participating devices.

The case for writing this framework in Rust

Rust has definite potential as a host language for machine learning itself. But, above, we already insisted on giving developers freedom of choice here. Hence, we selected Rust for other reasons.

Our framework for federated learning is not a framework for machine learning as such. Rather, it supports the federation of machine learning that takes place on possibly heterogeneous devices and where use cases involve many such devices.

The programming language in which this framework is written should therefore give us strong support for the following:

  • Compiles and runs "everywhere": The language should not require its own runtime and code should compile on a wide range of devices.
  • Memory and Concurrency Safety: Code that compiles should be both memory safe and free of data races.
  • Secure communication: State of the art cryptography should be available in vetted implementations.
  • Asynchronous communication: Abstractions for asynchronous communication should exist that make federated learning scale.
  • Fast and functional: The language should offer functional abstractions but also compile code into fast executables.

Rust is one of the very few choices of modern programming languages that meet these requirements:

  • Its concepts of Ownership and Borrowing make it both memory and thread-safe (hence avoiding potential concurrency issues).
  • It has a strong and static type discipline and traits, which describe shareable functionality of a type.
  • It has rich functional abstractions, for example the tower-service based on the foundational trait Service.
  • Its idiomatic code compares favorably to idiomatic C in performance.
  • It is widely deployable and doesn't necessarily depend on a runtime, unlike languages such as Java and their need for a virtual machine to run its code. Foreign Function Interfaces support calls from other languages/frameworks, including Dart, Python and Flutter.
  • And it compiles into LLVM, and so it can draw from the abundant tool suites for LLVM.

We love XayNet and would like to hear about your use of it

We feel blessed to have such a strong Engineering team that includes several senior Rust developers and folks who were eager to become experienced Rust programmers themselves! All of us are excited to share the fruits of this labor with you.

So without further ado, here is the release of XayNet, our federated learning framework written entirely in Rust. We hope you will like and use this framework. And we will be grateful for any feedback, contributions or news on your usage of XayNet in your own projects.

Getting Started

Running the platform

There are a few different ways to run the backend: via docker, or by deploying it to a Kubernetes cluster or by compiling the code and running the binary manually.

  1. Everything described below assumes your shell's working directory to be the root of the repository.
  2. The following instructions assume you have pre-existing knowledge on some of the referenced software (like docker and docker-compose) and/or a working setup (if you decide to compile the Rust code and run the binary manually).
  3. In case you need help with setting up your system accordingly, we recommend you refer to the official documentation of each tool, as supporting them here would be beyond the scope of this project:

Using docker-compose

The convenience of using the docker setup is that there's no need to setup a working Rust environment on your system, as everything is done inside the container.

Start the coordinator by pointing to the docker/docker-compose.yml file. Keep in mind that given this is the file used for development, it spins up some infrastructure that is currently not essential.

docker-compose -f docker/docker-compose.yml up --build

If you would like, you can use the docker/docker-compose-release.yml file, but keep in mind that given this runs a release build with optimizations, compilation will be slower.

docker-compose -f docker/docker-compose-release.yml up --build

Using Kubernetes

To deploy an instance of the coordinator to your Kubernetes cluster, use the manifests that are located inside the k8s/coordinator folder. The manifests rely on kustomize to be generated (kustomize is officially supported by kubectl since v1.14). We recommend you thoroughly go through the manifests and adjust them according to your own setup (namespace, ingress, etc.).

Remember to also check (and adjust if necessary) the default configuration for the coordinator, available at k8s/coordinator/development/config.toml.

Please adjust the domain used in the k8s/coordinator/development/ingress.yaml file so it matches your needs (you can also skip ingress altogether, just make sure you remove its reference from k8s/coordinator/development/kustomization.yaml).

Keep in mind that the ingress configuration that is shown on k8s/coordinator/development/ingress.yaml relies on resources that aren't available in this repository, due to their sensitive nature (TLS key and certificate, for instance).

To verify the generated manifests, run:

kubectl kustomize k8s/coordinator/development

To apply them:

kubectl apply -k k8s/coordinator/development

In case you are not exposing your coordinator via ingress, you can still reach it using a port-forward. The example below creates a port-forward at port 8081 assuming the coordinator pod is still using the app=coordinator label:

kubectl port-forward $(kubectl get pods -l "app=coordinator" -o jsonpath="{.items[0].metadata.name}") 8081

Building the project manually

The coordinator can be built and started with:

cargo run --bin coordinator --manifest-path rust/Cargo.toml -- -c configs/config.toml

Running the example

The example can be found under rust/src/bin/. It uses a dummy model but is network-capable, so it's a good starting point for checking connectivity with the coordinator.


Make sure you have a running instance of the coordinator and that the clients you will spawn with the command below are able to reach it through the network.

Here is an example on how to start 20 participants that will connect to a coordinator running on

RUST_LOG=xaynet=info cargo run --bin test-drive-net -- -n 20 -u


If you have any difficulties running the project, please reach out to us by opening an issue and describing your setup and the problems you're facing.


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