Collaborative edge and cloud learning: Potentials and solutions

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The data generated and contributed by today's personal computing devices, ranging from text, voice, to pictureand video, are enormous and contain invaluable information worth discovering. Recent advances in deep neuralnetworking have shown great potentials in exploring the hidden information therein. Deep learning relies onstrong computation power to process the massive amount of data, which is typical offered by machine clusters,or more general, modern data centers. While cloud-centric learning works well for the data that are available indatacenters, gathering the data from worldwide sources unavoidably incurs high traffic and, more importantly,latency that challenges such realtime learning applications as face recognition and human tracking in cameranetworks. The concept of edge computing has been recently advocated as a complement to cloud computing. Itpushes applications, data, and computing content away from the centralized data centers. As such, it cansignificantly accelerate the training process by reducing the traffic transferred to the cloud and the inferencelatency for a broad spectrum of deep learning applicationsHuawei is an industrial pioneer in building the communication and computation infrastructure for edgecomputing, and has started building its public cloud infrastructure as well. In this project, we will worktogether to understand the state-of-the-art of collaborative edge and cloud learning. We will identify theopportunities and challenges on how to push data pre-processing and feature extracting to the network edge.We will then develop novel solutions that minimize the network traffic and inference latency yet with desiredaccuracy for deep learning.