Distributed Optimization and Federated Learning in Emerging Smart Networks
December 2020 – November 2022
This project aims to solve theoretical and practical challenges in distributed optimization and learning in smart networked systems. We wish to design fast and practical algorithms that have theoretical convergence guarantees. We are focusing on two concrete topics: 1) safe resource allocation in power networks to avoid systems breakdown and 2) efficient asynchronous parallel and distributed optimization (better step sizes and delay-tolerant algorithm design).
Networked systems such as power networks and IoT systems are important in our life. To make these systems “smart” (e.g., saving cost or improving utility), we need to learn models from data, equivalent to solving optimization problems. Consequently, designing efficient algorithms to solve optimization problems in these systems is of strong practical significance. Moreover, theoretical convergence analysis is also dispensable to these algorithms to guarantee their reliability.
About the Digital Futures Postdoc Fellow
Xuyang Wu is a Postdoctoral researcher at KTH Digital Futures, co-supervised by Prof. Mikael Johansson at KTH (DCS, EECS) and Prof. Sindri Magnússon at SU (DSV). He received a B.S. degree in Applied Mathematics from Northwestern Polytechnical University, China, in 2015 and a PhD in Communication and Information Systems at the University of Chinese Academy of Sciences, China, in 2020. He was a finalist for the best student paper award at IEEE ICCA 2019. His current research interests include distributed optimization and federated learning. In particular, he focuses on algorithmic foundations, convergence analysis, and resource efficiency in emerging systems such as IoT and cyber-physical systems. More information can be found on his homepage: http://xuyangwu.github.io/
Mikael Johansson, Professor, Division of Decision and Control Systems, School of EECS, KTH
Sindri Magnússon, Associate Professor, Department of Computer and Systems Science, Stockholm University
Watch the recorded presentation at Digitalize in Stockholm 2022 event:
Digital Futures Postdoctoral Fellow, Postdoc project: Distributed Optimization and Federated Learning in Emerging Smart Networksxuyangw@kth.se
Professor, Division of Decision and Control Systems at KTH EECS, Supervisor for postdoc project Distributed Optimization and Federated Learning in Emerging Smart Networks, Digital Futures Faculty+46 8 790 74 36
Associate professor, Department of Computer and Systems Sciences at Stockholm University, Vice Chair Working group Cooperate, Co-PI of research project Decision-making in Critical Societal Infrastructures (DEMOCRITUS), Co-PI of research project Data-Driven Control and Coordination of Smart Converters for Sustainable Power System Using Deep Reinforcement Learning at C3.ai DTI, Co-Supervisor for postdoc project Distributed Optimization and Federated Learning in Emerging Smart Networks, Digital Futures Faculty+46 8 16 11 15