面向移动键盘预测的联邦学习
Federated Learning for Mobile Keyboard Prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon 等 9 位作者
AI 速览：作者在智能手机虚拟键盘的下一词预测任务上，用联邦平均算法在客户端设备上训练循环神经网络语言模型，并与服务器端随机梯度下降训练进行比较，报告联邦方法取得更好的预测召回率。
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones. Server-based training using stochastic gradient descent is compared with training on client devices using the Federated Averaging algorithm. The federated algorithm, which enables training on a higher-quality dataset for this use case, is shown to achieve better prediction recall. This work demonstrates the feasibility and benefit of training language models on client devices without exporting sensitive user data to servers. The federated learning environment gives users greater control over the use of their data and simplifies the task of incorporating privacy by default with distributed training and aggregation across a population of client devices.