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原北邮博士师兄组织的Wokshop。还请大家多多关照。Workshop 网址: https://icc2020.ieee-icc.org/workshop/ws-14-workshop-edge-machine-learning-5g-mobile-networks-and-beyond。以下是我复制过来的。 WELCOME TO THE 1ST WORKSHOP ON "EDGE MACHINE LEARNING FOR 5G MOBILE NETWORKS AND BEYOND" June 11, 2020, Dublin, Ireland WORKSHOP CO-CHAIRS: Mingzhe Chen, Chinese University of Hong Kong, Shenzhen, China, and Princeton University, NJ, USA (mingzhec@princeton.edu) Zhaohui Yang, King’s College London, UK (yang.zhaohui@kcl.ac.uk) Kaibin Huang, University of Hong Kong, Hong Kong (huangkb@eee.hku.hk) STEERING COMMITTEE MEMBERS: Prof. Mérouane Debbah, IEEE Fellow, Huawei France Research Center and Mathematical and Algorithmic Sciences Lab, France Prof. Zhu Han, IEEE Fellow, University of Houston, TX, USA Prof. H. Vincent Poor, IEEE Fellow, Princeton University, NJ, USA KEYNOTE SPEAKERS: Prof. Mehdi Bennis, University of Oulu, Finland Prof. Walid Saad, Virginia Tech, USA. SCOPE AND TOPICS OF THE WORKSHOP Machine learning and data-driven approaches have recently received much attention as a key enabler for future 5G and beyond wireless networks. To date, most existing learning solutions for wireless networks have relied on conventional machine learning approaches that require centralizing the training data and inference processes on a single data center. However, in future intelligent wireless networks, due to privacy constraints and limited communication resources for data transmission, it is impractical for all wireless devices that are engaged in learning to transmit all of their collected data to a data center that can subsequently use a centralized learning algorithm for data analytics or network self-organization. To this end, distributed edge learning frameworks are needed, to enable the wireless devices to collaboratively build a shared learning model with training their collected data locally. For wireless communication, edge machine learning admits many use cases. For example, distributed multi-agent reinforcement learning algorithms can be used to solve complex convex and nonconvex optimization problems that arise in various use cases such as network control, user clustering, resource management, and interference alignment. Moreover, distributed federated learning enables users to collaboratively learn a shared prediction model while remaining their collected data on their devices for user behavior predictions, user identifications, and wireless environment analysis. The field of edge machine learning is still at its infancy as there are many open theoretical and practical problems yet to be addressed, for edge machine learning, in general, and for wireless communication systems, in particular. Thus, this full-day workshop will seek to bring together researchers and experts from academia, industry, and governmental agencies to discuss and promote the research and development needed to overcome the major challenges that pertain to this cutting-edge research topic. Suitable topics for this workshop include, but are not limited to, the following areas: Fundamental limits of edge machine learning systems Wireless network optimization for improving the performance of edge machine learning Readio rsource management for edge machine learning Multiple access for edge machine learning Data compression for edge machine learning Adaptive transmission for edge machine learning Techniques for wireless crowd labelling Interference management in edge machine learning networks Emerging theories and techniques such as age of information and blockchain for edge machine learning Modeling and performance analysis of edge machine learning networks Energy efficiency of implementing machine learning over wireless edge networks Ultra-low latency edge machine learning Data analytics driven wireless communication Multi-agent reinforcement learning for intelligent network control and optimization Network architectures and communication protocols for edge machine learning Experimental testbeds and techniques of edge machine learning Privacy and security issues of edge machine learning Edge machine learning for intelligent signal processing, e.g., signal detection Edge machine learning for mobile user behavior analysis and inference Edge machine learning for emerging applications, e.g., vehicle to everything (V2X), UAV-enabled communication, Internet of Things, intelligent reflecting surface (IRS), Massive MIMO, virtual reality (VR), and augmented reality (AR) IMPORTANT DATES Paper submission January 20, 2020 Notification of acceptance February 20, 2020 Final papers submission March 1, 2020 Workshop date June 11, 2020

【征稿】ICC 2020 Workshop on Edge Machine Learning for 5G