Cong Shen, assistant professor of electrical and computer engineering at the University of Virginia School of Engineering, earned the best paper award in signal processing for communications at the International Conference on Communications—the annual flagship event of the Institute of Electrical and Electronics Engineers Communications Society.
Shen demonstrated a way to shrink machine learning models held and exchanged through a wireless communications network. The method can compress a machine learning model to one-sixteenth its original size while preserving performance.
With this method, wireless devices like smart phones can train machine learning models locally. Training data remains on the device, which in turn promotes data privacy, reduces vulnerabilities to adversarial attack and lowers the overhead cost of moving large data files from devices to a central server.
Shen collaborated with Xiang Chen, associate professor in the School of Electronics and Information Technology at Sun Yat-Sen University, Guangzhou, China. Chen’s Ph.D. student Sihui Zheng first-authored the team’s paper, Design and Analysis of Uplink and Downlink Communications for Federated Learning, published in full in the IEEE Journal on Selected Areas in Communication.