Broader Impacts

This project is centered around developing innovative edge learning algorithms over wireless MAC channels while taking into account the constraints in computing, power and bandwidth therein, aiming to advance wireless edge learning in a variety of IoT applications, ranging from transportation, agriculture, buildings, energy, health care, smart connected manufacture, to the general AI-enabled systems of the future. Success will tackle the fundamental challenge in the rapid deployment of AI methods, namely how to effectively carry out computation and coordination for edge learning while taking into account the constraints in wireless networks and computational resources at billions of edge devices. More broadly, this work promotes reliable and secure connected IoT systems, across data acquisition, across computation, and across communication. The broader impact from this research will also come through many educational opportunities enabled through this research. The team is committed to providing opportunities in STEM to K-12, women, and under-represented minorities, and has a good track record in this regard. Moreover, the close collaboration of this team between academia and industry promises a fast and effective transition of academic results to industry practice, making a real-world impact.