• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Anderes
  4. Fed-DART and FACT: A solution for Federated Learning in a production environment
 
  • Details
  • Full
Options
2022
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Title

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

Title Supplement
Published on arXiv
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.
Author(s)
Weber, Nico  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Holzer, Patrick  
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Jacob, Tania
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Ramentol Martinez, Enislay
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
DOI
10.48550/arXiv.2205.11267
Language
English
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM  
Fraunhofer Group
ICT
Keyword(s)
  • Federated Learning

  • FACT based

  • Fed-DART

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024