8th International Conference on Machine Learning for Networking (MLN'2025)
Keynote: Stefano Secci, Professor at Conservatoire National des Arts et Métiers, France
Title: Challenges towards in-network learning: algorithms and systems
Abstract
Discuss potential approaches to overcoming them. We will introduce a closed-loop automation framework that spans from network data pipelining -- designed to power in-network artificial intelligence nodes -- to distributed anomaly detection modules and node orchestration. Preliminary results will be presented and discussed. Additionally, we will present a 5G use case and introduce the recently updated 5G3E dataset, which aims to advance research in in-network learning.
Biography
Stefano Secci is professor at Cnam, Paris, since 2018, responsbile of research and teaching activities in networking. He previously held positions at Sorbonne University as associate professor, at George Mason and NTNU Universities as postdoc researcher, at Telecom ParisTech and Politecnico di Milano as Ph.D candidate, and at CNIT and Fastweb as engineer. He graduated from Politecnico di Milano in telecommunications engineering in 2005. His current research interests are about network automation and distributed learning.