Fed-DART and FACT: A solution for Federated Learning in a production environment
This addresses the problem of bringing Federated Learning to production for generating business impact, though it appears incremental as it builds on existing methods.
The paper tackles the challenge of deploying Federated Learning in production environments by developing the FACT framework based on Fed-DART, which enables easy and scalable deployment to leverage private and decentralized data.
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.