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

• URL: http://arxiv.org/abs/2205.11267v1
• Date: Mon, 23 May 2022 12:32:38 GMT
• Title: Fed-DART and FACT: A solution for Federated Learning in a production environment
• Authors: Nico Weber, Patrick Holzer, Tania Jacob, Enislay Ramentol
• Abstract summary: Decentralized artificial intelligence (AI) solution solves a variety of problems in industrial applications. Bringing AI to production for generating a real business impact is a challenging task. We have developed an innovative Federated Learning framework FACT based on Fed-DART.
• Score: 0.30586855806896046
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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