Tieniu Tan
Nanjing University,
China
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Yonggang Wen
Nanyang Technological University, Singapore
Bio: Dr. Yonggang Wen is a Professor and President’s Chair in Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He also serves as the Associate Vice President (Capability Building) at NTU Singapore. Previously he served as the Associate Dean (Research) at College of Engineering (2018-2023), the acting Director for Nanyang Technopreneurship Center (NTC) (2017-2019) and the Assistant Chair (Innovation) at the School of Computer Science and Engineering (2016-2018), at NTU Singapore. He received his PhD degree in Electrical Engineering and Computer Science (minor in Western Literature) from Massachusetts Institute of Technology (MIT), Cambridge, USA, in 2008. Dr. Wen has published over 300 papers in top journals and prestigious conferences. His systems research has gained global recognitions. His work in Multi-Screen Cloud Social TV has been featured by global media (more than 1600 news articles from over 29 countries) and received ASEAN ICT Award 2013 (Gold Medal). His work on Cognitive Digital Twin for Data Centre, has won the 2015 Data Centre Dynamics Awards – APAC (the ‘Oscar’ award of data centre industry), 2016 ASEAN ICT Awards (Gold Medal), 2020 IEEE TCCPS Industrial Technical Excellence Award, 2021 W.Media APAC Cloud and Datacenter Technology Leader Award, and 2022 Singapore Computer Society Digital Achiever Tech Leader Award. He was the winner of 2019 Nanyang Research Award and the sole winner of 2016 Nanyang Awards for Innovation and Entrepreneurship, both of which are the highest recognition at NTU. He is a co-recipient of multiple Best Paper Awards from top journals, including 2019 IEEE TCSVT and 2015 IEEE Multimedia, and at international conferences, including 2016 IEEE Globecom, 2016 IEEE Infocom MuSIC Workshop, 2015 EAI Chinacom, 2014 IEEE WCSP, 2013 IEEE Globecom and 2012 IEEE EUC. He is the Editor in Chief of IEEE Transactions on Multimedia (TMM), serves or has served on editorial boards for multiple IEEE and ACM transactions, and was elected as the Chair for IEEE ComSoc Multimedia Communication Technical Committee (2014-2016). His research interests include cloud computing, green data center, big data analytics, multimedia network and mobile computing. He is a Fellow of IEEE and Singapore Academy of Engineering.
Title: EasyFL: Optimising Federated Learning for Computer Vision Applications
Abstract: Deep learning has transformed industries through powerful computer vision applications. However, the traditional centralized training approach is facing serious challenges due to ever-increasing data privacy regulations. To mitigate this problem, Federated Learning (FL) has emerged as a distributed training paradigm that trains deep learning models on user devices, protecting data privacy by eliminating the need for data transfer to a central server. Despite FL’s significant potential for training computer vision applications, it is still in its early stage and requires further optimization in terms of system performance and specificity for booming computer vision applications.
In this talk, we focus on how to optimize FL platforms for computer vision applications through system and algorithmic optimizations. We begin by introducing our low-code FL platform, EasyFL, which improves researchers’ productivity and efficiency in implementing new federated computer vision applications. It allows users to write less code with 1.5 times of training speedup. Built on EasyFL, we then present multiple algorithmic optimizations to improve accuracy for various computer vision applications, including person re-identification, face recognition, and self-supervised learning. Finally, we present algorithmic and system optimizations for training multiple simultaneous FL activities under resource constraints.
Mihai Datcu
German Aerospace Research Center, Germany
Bio: Prof. Mihai Datcu is Senior Scientist at the Remote Sensing Technology Institute, German Aerospace Research Center (DLR). His research concerns theoretical aspects of information theory, Bayesian inference, computational sensing and artificial intelligence. His team has received international recognition in the areas of data mining, image perception, and semantic extraction for very high resolution earth observation data.
Mihai Datcu is an IEEE Fellow. He was awarded the prestigious IEEE GRSS David Landgrebe Award at the International Geoscience and Remote Sensing Symposium (IGARSS) in Kuala Lumpur in 2022. It was in recognition of his outstanding contribution to the study of earth observation data using innovative concepts to analyze big data, image mining, machine learning, smart sensors and quantum resources.
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