Supercharging Federated Learning with Flower and NVIDIA FLARE

TL;DR

This paper presents an initial integration of Flower and NVIDIA FLARE, two open-source federated learning systems, to enhance the FL ecosystem by enabling seamless interoperability and improving efficiency for FL applications.

Contribution

The paper introduces a novel integration of Flower and FLARE, allowing FL applications to operate across both platforms without modifications, streamlining development and deployment.

Findings

Seamless interoperability between Flower and FLARE achieved.

Integration reduces complexity in deploying FL applications.

Enhanced efficiency and accessibility of federated learning applications.

Abstract

Several open-source systems, such as Flower and NVIDIA FLARE, have been developed in recent years while focusing on different aspects of federated learning (FL). Flower is dedicated to implementing a cohesive approach to FL, analytics, and evaluation. Over time, Flower has cultivated extensive strategies and algorithms tailored for FL application development, fostering a vibrant FL community in research and industry. Conversely, FLARE has prioritized the creation of an enterprise-ready, resilient runtime environment explicitly designed for FL applications in production environments. In this paper, we describe our initial integration of both frameworks and show how they can work together to supercharge the FL ecosystem as a whole. Through the seamless integration of Flower and FLARE, applications crafted within the Flower framework can effortlessly operate within the FLARE runtime environment without necessitating any modifications. This initial integration streamlines the process, eliminating complexities and ensuring smooth interoperability between the two platforms, thus enhancing the overall efficiency and accessibility of FL applications.