Decentralized Machine Learning

Blaise Agüera y Arcas

Abstract: Summary form only given. In the past decade we have seen very rapid growth in two fields: cloud services, and neural networks. These two are connected, in that logs from services are the fuel that has powered data-hungry deep learning algorithms. However, there are several forces on the other side of the coin, pushing neural capabilities onto the device and out of the cloud. These include: the development of power-efficient on-device neural processors; scaling laws relating energy density, size, and bandwidth; and an increasing demand for data privacy. This talk will address these trends, technologies designed to address them (including Federated Learning, quantization, and device-friendly architectures like MobileNet), and the product landscape emerging from these new developments.