Timely Communication
Timely communication, encompassing efficient information exchange and coordinated action, is a crucial research area impacting diverse fields from AI to healthcare. Current research focuses on optimizing communication efficiency in distributed systems like federated learning, employing techniques such as scalar communication, zero-order optimization, and lossy compression to reduce bandwidth and energy consumption. This work also explores the development of robust communication strategies in noisy environments and the integration of AI, particularly LLMs, to enhance understanding and improve the efficiency of communication in various applications, including human-robot collaboration and crisis response. The resulting advancements have significant implications for improving the performance and scalability of AI systems, optimizing resource utilization in communication networks, and enhancing human-computer and human-robot interaction.
Papers
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Manh V. Nguyen, Liang Zhao, Bobin Deng, William Severa, Honghui Xu, Shaoen Wu
Learning to Coordinate without Communication under Incomplete Information
Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo, Ufuk Topcu
Declarative Integration and Management of Large Language Models through Finite Automata: Application to Automation, Communication, and Ethics
Thierry Petit, Arnault Pachot, Claire Conan-Vrinat, Alexandre Dubarry
Learning Robust Representations for Communications over Noisy Channels
Sudharsan Senthil, Shubham Paul, Nambi Seshadri, R. David Koilpillai
Assessing the role of clinical summarization and patient chart review within communications, medical management, and diagnostics
Chanseo Lee, Kimon-Aristotelis Vogt, Sonu Kumar
Semantic Revolution from Communications to Orchestration for 6G: Challenges, Enablers, and Research Directions
Masoud Shokrnezhad, Hamidreza Mazandarani, Tarik Taleb, Jaeseung Song, Richard Li