论文标题
美联储和事实：生产环境中联合学习的解决方案
Fed-DART and FACT: A solution for Federated Learning in a production environment
论文作者
论文摘要
作为分散的人工智能（AI）解决方案联合学习解决了工业应用中的各种问题。它启用了一个不断的自我提高的AI，可以在边缘到处部署。但是，将AI带入生产以产生实际的业务影响是一项艰巨的任务。特别是在联邦学习的情况下，需要多个领域的专业知识和资源才能实现其全部潜力。考虑到这一点，我们基于Fed-Dart开发了创新的联合学习框架事实，从而实现了简单且可扩展的部署，从而帮助用户充分利用其私人和分散数据的潜力。
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.
