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
Make me an Offer: Forward and Reverse Auctioning Problems in the Tourism Industry
Ioannis T. Christou, Dimitris Doukas, Konstantina Skouri, Gerasimos Meletiou
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control
Tong Liu, Lei Lei, Kan Zheng, Xuemin, Shen
Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part II: Control-Aware Radio Resource Allocation
Lei Lei, Tong Liu, Kan Zheng, Xuemin, Shen