联合学习
计算机科学
素描
可扩展性
开放域
领域(数学分析)
比例(比率)
人工智能
学习设计
人机交互
分布式计算
数据库
答疑
数学分析
数学教育
物理
算法
量子力学
数学
作者
Keith Bonawitz,Hubert Eichner,Wolfgang Grieskamp,Dzmitry Huba,Alex Ingerman,Vladimir Ivanov,Chloé Kiddon,Jakub Konečný,Stefano Mazzocchi,H. Brendan McMahan,Timon Van Overveldt,David Petrou,Daniel Ramage,Jason Roselander
出处
期刊:Cornell University - arXiv
日期:2019-02-04
被引量:286
摘要
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.
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