{"id":3129,"date":"2021-02-03T14:43:39","date_gmt":"2021-02-03T14:43:39","guid":{"rendered":"https:\/\/www.digitalfutures.kth.se\/?page_id=3129"},"modified":"2023-11-09T09:16:17","modified_gmt":"2023-11-09T08:16:17","slug":"postdoc-project-distributed-optimization-and-federated-learning-in-emerging-smart-networks","status":"publish","type":"page","link":"https:\/\/wpmu-tris.sys.kth.se\/digitalfutures\/research\/postdoc-fellowships\/completed-postdoc-fellowships\/postdoc-project-distributed-optimization-and-federated-learning-in-emerging-smart-networks\/","title":{"rendered":"Distributed Optimization and Federated Learning in Emerging Smart Networks"},"content":{"rendered":"
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December 2020 – November 2022<\/p>\n

Objective<\/em>
\nThis 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).<\/p>\n

Background
\n<\/em>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.<\/p>\n

About the Digital Futures Postdoc Fellow
\n<\/em>Xuyang Wu<\/strong> is a Postdoctoral researcher at KTH Digital Futures, co-supervised by Prof. Mikael Johansson at KTH (DCS, EECS) and Prof. Sindri Magn\u00fasson 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\u00a0 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.\u00a0 More information can be found on his homepage: http:\/\/xuyangwu.github.io\/<\/a><\/p>\n

Main supervisor
\n<\/em>Mikael Johansson<\/strong>, Professor, Division of Decision and Control Systems, School of EECS, KTH<\/p>\n

Co-supervisor<\/em>
\nSindri Magn\u00fasson<\/strong>, Associate Professor, Department of Computer and Systems Science, Stockholm University<\/p>\n

Watch the recorded presentation at Digitalize in Stockholm 2022 event:<\/strong><\/p>\n