Efficient Distributed Machine Learning: Joint Algorithm and System Approach
Efficient Distributed Machine Learning: Joint Algorithm and System Approach
Distributed machine learning is gaining popularity due to its advantages in flexibility, scalability, and privacy. However, it inevitably causes large latency and resource waste due to geographically distributed data and heterogeneous devices. In this talk, I will present our recent research progress in addressing such issues through our joint algorithm and system designs. By simplifying AI models and developing faster convergence training algorithms, we observe significant reductions in latency and resource consumption; by coordinating computing, communication, and other resources, both model training and inference are significantly accelerated. We also adapt our design to diverse systems and fluctuating environments to maximise the potential of both algorithms and systems. I will cover several exemplary research papers, such as efficient federated learning, collaborative video analytics, and the scheduling algorithms that support them, to demonstrate the effectiveness of our designs.
Click the link below to join the seminar at the following time
Melbourne (AEDT) : 6 Feb 2024 2:00 PM
Japan (JST) : 6 Feb 2024 12:00 PM
China (CST) : 6 Feb 2024 11:00 AM
India (IST) : 6 Feb 2024 8:30 AM
Central Europe (CET) : 6 Feb 2024 4:00 AM
New York (EST) : 5 Feb 2024 10:00 PM
Los Angeles (PST) : 5 Feb 2024 7:00 PM
Webinar passcode: 839196 (if asked when joining the seminar)
About the speaker
Senior Lecturer, University of Sydney
Wei Bao received the PhD degree in Electrical and Computer Engineering from the University of Toronto, Canada, in 2016. He is currently a senior lecturer at the School of Computer Science, the University of Sydney. His research covers networking, edge computing, and distributed machine learning. He received the Best Paper Awards in ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM) in 2013 and 2019 and IEEE International Symposium on Network Computing and Applications (NCA) in 2016. He is co-CI of two ARC Discovery Projects and lead CI of one ARC Linkage Project.
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