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Wednesday, April 6, 2022 – 8:00AM to 9:00AM
Presenter
Shenghong Dai, University of Wisconsin Madison
ABSTRACT:
More vehicles are equipped with sensors that could collect massive data of wide-area environments. To leverage such data and protect clients' privacy, we naturally think of Federated Learning (FL) which trains a high-quality shared model through training decentralized data over clients and sending back only the model updates. However, a bottleneck might occur over the central server with a large amount of clients. This limitation motivates the need for decentralized FL where clients share their model updates with their neighbors instead of the central coordinator. We present a new decentralized FL algorithm with convergence guarantees to address two challenges: a) clients do not have static data but dynamically changing data; b) the connectivity graph is changing over time (e.g., nearby vehicles are not fixed). Finally, we develop a new decentralized FL simulator that provides a realistic modeling for the dynamic graph.
BIO:
ooShenghong Dai is a Ph.D. candidate at the Department of Electrical and Computer Engineering at University of Wisconsin-Madison. Her research interests lie in the fields of machine learning, computer vision and mobile systems. In particular, her recent work focuses on distributed machine learning (e.g., federated learning) and on-device machine learning.
Zoom Link: https://duke.zoom.us/j/94209797351pwd=UXlVYWd1TzFQUkJENDdmVDhUMWVmQT09
Meeting ID: 942 0979 7351
Passcode: 718903
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