Private and Efficient On-Device Machine Learning

On-device Machine Learning applications provide a wealth of opportunities for sensing and analytics, particularly when cloud connectivity is not always readily available. Making these applications more energy-efficient and private can reduce their reliance on batteries and/or excessive data collection. Solutions in this space would have significant implications for a new generation of sensing and monitoring applications for environmental monitoring, population-wide analytics, scientific exploration, and climate/weather prediction. In this talk I will provide an overview of recent attempts in this space, and challenges ahead for providing reliable, secure, and private client-side applications.