Attend LIONS seminar with speaker Nicolo Michelusi, Sept. 25
About:
Join the Learning, Information, Optimization, Networks and Statistics, or LIONS, group for a seminar with Nicolo Michelusi, associate professor in the School of Electrical, Computer and Energy Engineering, part of the Ira A. Fulton Schools of Engineering at Arizona State University. He received his doctorate from the University of Padova, Italy, and the M.Sc. degree in telecommunications engineering from the Technical University of Denmark as part of the T.I.M.E. double degree program. Before joining ASU, he was a postdoctoral research fellow at the University of Southern California and an assistant professor at Purdue University.
His research interests include wireless communications and networks, machine learning over wireless systems, decentralized and federated learning and stochastic optimization. Michelusi is a senior member of the Institute of Electrical and Electronics Engineers, or IEEE. He received the NSF CAREER Award in 2021, the IEEE Communication Theory Technical Committee Early Achievement Award in 2022, the IEEE Communications Society William R. Bennett Prize in 2024 and an IEEE ICC 2025 Best Paper Award for his work on non-coherent over-the-air federated learning.
Abstract:
Massive networks of wireless devices offer new opportunities for large-scale machine learning, but their decentralized nature and resource-limited wireless connectivity pose fundamental challenges. This talk will discuss fully decentralized learning systems in which devices collaboratively train a model without relying on a central coordinator. The talk will focus on decentralized gradient descent, whose standard implementation requires consensus over a communication graph. While effective in principle, its naive wireless implementation requires topology knowledge, channel-aware control, and interference management, making it difficult to scale efficiently. The talk will then present a non-coherent over-the-air consensus scheme that exploits, rather than avoids, simultaneous wireless transmissions. By using energy superposition, the method enables one-shot consensus without channel state information or explicit knowledge of the graph topology.
Recent extensions of this approach will also be discussed, including robustness to external interference and operation over broader classes of channel models beyond Rayleigh flat fading. The talk will present the core algorithmic ideas, convergence guarantees and numerical results for decentralized learning over wireless networks.
Seminar host:
Our host for this LIONS Seminar is Gautam Dasarathy, associate professor in the Fulton Schools at ASU.
Visit the LIONS Seminar website for more information.
LIONS Seminar
Friday, Sept. 25, 2026
1:30–2:30 p.m.
Goldwater Center for Science and Engineering (GWC) 487, Tempe campus [map]