Department of Electrical Engineering & YINS Talk
Speaker: Shiqiang Wang, IBM T.J. Watson Research Center
Title: Federated Machine Learning in Resource-Constrained Edge Computing Systems
Host: Professor Leandros Tassiulas
Abstract:
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this talk, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
Bio:
Shiqiang Wang is a Research Staff Member in the AI@Edge Research Group at IBM T. J. Watson Research Center. He received his Ph.D. at Imperial College London in 2015. His current research focuses on theoretical and practical aspects of mobile edge computing, cloud computing, and machine learning. He has over 40 scholarly publications. He serves as an associate editor for IEEE Access and has served as a technical program committee (TPC) member or reviewer for a number of international conferences and journals. He received the 2015 Best Student Paper Award of the Network and Information Sciences International Technology Alliance (ITA). For more details, please visit his homepage at http://researcher.watson.ibm.com/researcher/view.php?person=us-wangshiq