07 May, 2021 (Fri)Time:
4:00 pmWebinar Link:
SpeakerMr. Zezhong Zhang
Department of Electrical and Electronic Engineering
The University of Hong Kong
Recently years, the attempts on distilling mobile data into useful knowledge have been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the linear structure of a dataset, which is useful for feature extraction and data compression. In this work, we propose the deployment of distributed PCA over a multi-access channel based on the algorithm of stochastic gradient descent to learn the dominant feature space of a distributed dataset at multiple devices. Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA. The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA. The idea is materialized by proposing a power-control scheme controlling the level of channel noise accordingly. The scheme is proved to achieve a faster convergence rate than in the case without power control by experiments on real datasets.
Zoom Link (during COVID-19 special period):
Biography of the speaker:
Zezhong Zhang received the B.S.E. degrees in electrical and electronic engineering from Southern University of Science and Technology in 2017. He is currently pursuing the Ph.D. degree with the Department of EEE, The University of Hong Kong. His research interests include distributed machine learning, joint communication and sensing, and 5G technologies, such as device-to-device communication and massive MIMO.
All are welcome.
OrganizerDr. K.B. Huang
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