IEEE North Jersey Section

Abstract Fog learning is an emerging paradigm for optimizing the orchestration of artificial intelligence services over contemporary network systems. Different from existing distributed techniques such as federated learning, fog learning emphasizes intrinsically in its design the unique node, network, and data properties encountered in today’s fog networks that span computing elements from the edge to the cloud. An important thread of research in fog learning has been on understanding the role that local topologies formed on an ad-hoc basis among proximal groups of heterogeneous computing elements can play in elevating the achievable tradeoff between intelligence quality and resource efficiency. In this talk, I will discuss recent results on the analysis of fog learning processes which give insights into the impact that these topologies, along with other properties such as model characteristics and fog decision parameters, have on global training performance. Additionally, I will discuss the development of adaptive control methodologies that leverage such relationships for jointly optimizing relevant fog learning metrics. Distinguished Lecturer Series: https://www.comsoc.org/membership/distinguished-lecturers Speaker: https://www.comsoc.org/christopher-greg-brinton Co-sponsored by: North Jersey Information Theory Chapter Speaker(s): Chris Brinton, Agenda: 6:30-7:00pm Gather, Refreshments and Introduction 7:00-8:00pm Lecture 8:00-8:30pm Q&A, networking, wrap-up Room: Room # TBD, Bldg: TBD Building - Electrical and Computer Engineering, Rutgers University, TBD, Piscataway, New Jersey, United States, 08854

Special Presentation by Dr. Samson Lasaulce (Khalifa U., UAE) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 17 April 2025 @ 12:00 UTC Topic: Large Language Models (LLMs), Optimization, and Game Theory Abstract: In this talk, we will explore the interplay between large language models (LLMs) and optimization. After introducing a use case (consumption power scheduling) for which studying this interplay is fully relevant, we will survey the main approaches in this area, which include pure LLM-based approaches (e.g., to deal with math word problems) and combined approaches. Both limitations and promising solutions will be discussed. Application to radio resource management and to telecommunications more generally will also be addressed. In the last part of the talk, connections between LLMs and game theory will be discussed. Speaker: [] Samson Lasaulce is a Chief Research Scientist with Khalifa University. He is the holder of the TII 6G Chair on Native AI. He is also a CNRS Director of Research with CRAN at Nancy. He has been the holder of the RTE Chair on the "Digital Transformation of Electricity Networks". He has also been a part-time Professor with the Department of Physics at École Polytechnique (France). Before joining CNRS he has been working for five years in private R&D companies (Motorola Labs and Orange Labs). His current research interests lie in distributed networks with a focus on optimization, game theory, and machine learning. The main application areas of his research are wireless networks, energy networks, social networks, and now climate change. Dr Lasaulce has been serving as an editor for several international journals such as the IEEE Transactions. He is the co-author of more than 200 publications, including a dozen of patents and several books such as "Game Theory and Learning for Wireless Networks: Fundamentals and Applications". Dr Lasaulce is also the recipient of several awards such as the Blondel Medal award from the SEE French society.. Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/474729