TEPPIT Artifacts for SPHERE Testbed

Flower AI - related Artifacts

Introduction

Flower AI is a popular federated learning frameworks used by researchers and industry alike. We have prototyped several approaches to installing Flower on the SPHERE testbed and packaged them as SPHERE “artifacts” i.e. prepackaged code and data needed to reproduce a successful Flower installations.

Users will need to apply for a SPHERE accounts first (see Getting Started section) and get them activated before they can access the artifacts listed below.

Artifacts

1. Basic deployment of Flower framework in simulation on one node. This artifact is a basic installation that supports quickstart_pytorch simulation example from Flower documentation, described here.

2. Flower in deployment on two clients and a server. This artifact is a basic installation that supports quickstart_pytorch deployment on two supernodes (clients) and one superlink (server). It follows the example from Flower documentation, described here

3. Flower Interactive Jupyter Tutorial. This tutorial uses Jupyter Notebooks to showcase deployment of a Flower AI on SPHERE testbed. In this Jupyter tutorial, we show how to deploy a basic flower applicatoin on the SPHERE testbed. We use quickstart_pytorch example, described in this tutorial deployed over a SPHERE topology consisting of one server running SuperLink and several clients running Supernodes. It follows the quickstart instructions for distributed insecure installation.

   This tutorial is a self-documented, self-contained Jupyter notebook that users are encouraged to run cell-by-cell or just consult for ideas on how SPHERE deployment tasks can be scripted and automated.