KEYNOTE 1: DISTRIBUTED MACHINE LEARNING AT THE WIRELESS EDGE SPEAKER: PROF. DENIZ GÜNDÜZ IMPERIAL COLLEGE LONDON, UK Abstract: IoT devices collect significant amount of data at the wireless edge, opening up new potentials for machine learning applications. Current approach to edge intelligence is to offload all the collected data to a cloud server for central processing. This approach is not sustainable considering the expected growth in the number of IoT devices and the traffic they generate. Moreover, it creates significant privacy risks for the users, and introduces delays that cannot be tolerated by most applications. The alternative is to bring the intelligence to the edge, by distributing both the training and the inference tasks across edge devices and servers. In this talk, I will present recent results on efficient distributed inference and training over wireless channels taking into account channel impairments as well as power and bandwidth limitations of wireless devices. This will involve bringing together novel communication and coding techniques with distributed learning algorithms. SPEAKER: JULIEN FORGEAT, ARTIFICIAL INTELLIGENCE, ERICSSON RESEARCH Bio: Julien Forgeat is an artificial intelligence principal researcher at Ericsson Research. He joined Ericsson in 2010 after spending several years working on network analysis and optimization. He holds an M.Eng. in computer science from the National Institute of Applied Sciences in Lyon, France. At Ericsson, Julien has worked on mobile learning, Internet of Things and big data analytics before specializing in machine learning and AI infrastructure. His current research focuses on the software components required to run AI and machine learning workloads on distributed infrastructures as well as the algorithmic approaches that are best suited for complex distributed and decentralized use-cases.