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Title: FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
Date: 14 September 2022
Time: 8:00 AM Eastern (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Feng Yin
Based on the IEEE Xplore® article: FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
Published: IEEE Open Journal of Signal Processing, November 2020, available in IEEE Xplore®
Download article: The original article is available for download.
This SPS webinar will introduce a novel data-driven cooperative localization and location data processing framework, called FedLoc, in line with the emerging machine learning and optimization techniques. We first review two widely used learning models, namely the deep neural network model and the Gaussian process model, show their connections, and introduce various distributed model hyper-parameter optimization schemes that can be adopted to implement the federated learning. To give a complete picture, we then introduce some other vital ingredients of the FedLoc, including privacy protection schemes, wireless network infrastructures, etc. Lastly, we demonstrate various popular use cases covering a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, Spatio-temporal wireless traffic data prediction, etc. All the use cases aim at collaboratively building more accurate location services without sacrificing user privacy, particularly sensitive information related to their geographical trajectories. Future research directions will be discussed at the end of this webinar.
Dr. Feng Yin received the B.Sc. degree from Shanghai Jiao Tong University, China, and the M.Sc. and Ph.D. degrees from Technische Universitaet Darmstadt, Germany.
From 2014 to 2016, he was a postdoctoral researcher with Ericsson Research, Linkoping, Sweden. Since 2016, he has been an assistant professor with The Chinese University of Hong Kong, Shenzhen. His research interests include statistical signal processing, Bayesian learning and optimization, and sensory data fusion.
Dr. Yin was a recipient of the Chinese Government Award for Outstanding Self-Financed Students Abroad in 2013 and the Marie Curie Scholarship from the European Union in 2014. He was the finalist for the IEEE CAMSAP best paper award in 2013 and is an IEEE senior member currently serving as the Associate Editor for the Elsevier Signal Processing Journal.
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