作者: Blaise Aguera y Arcas , Daniel Ramage , H. Brendan McMahan , Seth Hampson , Eider Moore
DOI:
关键词: Mobile device 、 Data center 、 Term (time) 、 Data mining 、 Language model 、 User experience design 、 Stochastic gradient descent 、 Artificial intelligence 、 Principal (computer security) 、 Iterative and incremental development 、 Machine learning 、 Computer science
摘要: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the …