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
OML: Open, Monetizable, and Loyal AI
Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi, Anshul Nasery, Sandeep Nailwal, Sewoong Oh, Himanshu Tyagi, Pramod Viswanath
Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication
Shaba Shaon, Tien Nguyen, Lina Mohjazi, Aryan Kaushik, Dinh C. Nguyen