Fed-DART and FACT: A solution for Federated Learning in a production environment

TL;DR

This paper introduces Fed-DART and FACT, a federated learning framework designed for scalable, easy deployment in industrial environments to harness decentralized data for real-world AI applications.

Contribution

The paper presents FACT, a novel federated learning framework built on Fed-DART, facilitating scalable deployment and effective utilization of private decentralized data in production environments.

Findings

Enables scalable deployment of federated learning in industrial settings

Facilitates leveraging decentralized data for AI improvements

Supports deployment at the edge for real-time applications

Abstract

Federated Learning as a decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications. It enables a continuously self-improving AI, which can be deployed everywhere at the edge. However, bringing AI to production for generating a real business impact is a challenging task. Especially in the case of Federated Learning, expertise and resources from multiple domains are required to realize its full potential. Having this in mind we have developed an innovative Federated Learning framework FACT based on Fed-DART, enabling an easy and scalable deployment, helping the user to fully leverage the potential of their private and decentralized data.