“From Edge Video Analytics to Federated Learning“By Professor Dr. Ling Liu, School of Computer Science, Georgia Institute of Technology, USA |
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Abstract
The rapid growth of wireless mobile broadband communication networks has fueled new capabilities in scalable device-to-edge-to-cloud continuum, ranging from increased data rates of 1~10 Gbps, ultra-low latencies of 1ms or less, larger coverage with massive number of devices connected 24×7. These advances have enabled exciting new edge native applications, such as Augmented Reality/Virtual Reality (AR/VR) and video analytics. In this keynote, I will describe edge video analytics and federated learning as two emerging and complimentary distributed learning paradigms in navigating this device-to-edge-to-cloud continuum, while considering resilience, privacy and multi-tenancy of shared and heterogeneous resources. Edge video analytics is widely recognized as a killer application of edge computing. It deals with supporting scalable video analytics on heterogeneous edge devices for ultra-low latency, improved bandwidth, and faster data rates. We describe some Quality of Experience (QoE) guided data reduction techniques and discuss some open challenges for edge video analytics. Federated learning (FL) is an emerging distributed AI/ML paradigm, which decouples an iterative AI/ML model training into a distributed joint training by a geographically decentralized population of clients with heterogeneous and intermittently connected edge devices. Although FL allows its clients to keep sensitive training data local on their edge devices and only share local model updates with the federated server, it suffers from privacy leakages and data poisoning risks due to compromised clients. This keynote will advocate combining multiple innovative ideas and techniques synergistically to design scalable and resilient device-to-edge-to-cloud continuum for next generation applied computing systems.
Bio
Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of big data powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals and served as the editor in chief of IEEE Transactions on Service Computing (2013-2016), and currently is the editor in chief of ACM Transactions on Internet Computing (since 2019). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs, IBM and CISCO.