摘要
记者从发明人提供的背景资料中获得以下引述：“机器学习的联邦学习(ML)模型(s)是一种训练ML模型(s)的技术，其中设备上的ML模型本地存储在用户的客户端设备上，全局ML模型，即设备上ML模型的基于远程的Counter部分被远程存储在远程系统(例如，远程服务器或远程服务器群集。客户端设备，使用O n-Device ML模型，可以处理客户端设备检测到的用户输入，生成预测输出，并生成梯度以监督或非监督方式基于预测输出。
Abstract
Reporters obtained the following quote from the background information supplied by the inventors:“Federated learning of machine learning (ML) model(s) is a tec hnique for training ML model(s) in whichan on-device ML model is stored locally on a client device of a user, and a global ML model, that is aremote-based cou nterpart of the on-device ML model, is stored remotely at a remote system (e.g., aremote server or a cluster of remote servers). The client device, using the o n-device ML model, canprocess user input detected at the client device to gener ate predicted output, and can generate a gradientbased on the predicted output in a supervised or unsupervised manner.
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