Collaborative Research: CPS Medium: Learning through the Air: Cross-Layer UAV Orchestration for Online Federated Optimization

Abstract

Propelled by the growth in demand for artificial intelligence-enabled applications, the past decade has witnessed the emergence of Collaborative Cyber-Physical Learning Systems (CCPLS). CCPLS carry out distributed, learning-based processing tasks through coordination among Cyber-Physical System (CPS) devices, and are envisioned to provide critical functionality across the commercial and defense sectors in the next several years. However, the data generated by CCPLS is often large-scale, high-dimensional, heterogeneous, and time-varying, which poses critical challenges for intelligence modeling. Concurrently, unmanned vehicles, in particular Unmanned Aerial Vehicles (UAVs), have shown promise of scaling up information-sharing in CCPLS, especially in under-served regions such as rural areas. The project's novelties are in establishing a concrete foundation for UAV-CCPLS integration that unifies the associated learning, networking, and communication design aspects around appropriate intelligence metrics. The project's impacts are the development of UAV-assisted CCPLS for smart agriculture tasks, as well as advancing the manufacturing of UAVs and other unmanned vehicles tailored for CCPLS. Project outcomes will be disseminated by releasing open-source software and research videos and tutorials. The investigators will further engage in Curriculum development, diversity, and outreach activities including mentoring undergraduate researchers.

Research investigations center around three interconnected thrusts. Thrust 1 develops a novel UAV-assisted intelligence framework for CCPLS and introduces a precise, task-oriented notion of data dynamics and heterogeneity. Additionally, this thrust develops a `learning for learning? framework that aims to predictively estimate the rate of data dynamics. Thrust 2 investigates methodologies for jointly optimizing resource utilization and intelligence quality through co-design of UAV trajectories and UAV-to-CPS network establishment. The data dynamics framework from Thrust 1 is integrated into this design through an online, network-aware sequential decision-making framework. Finally, Thrust 3 develops CCPLS communication protocols based on learning-aware uplink and downlink wireless beamformers and over-the-air aggregation methods. These protocols are tailored to the specific needs of the UAV-assisted learning systems, e.g., the transmission of noisy information over UAV-to-UAV and UAV-to-access point communication links.

Cong Shen

Cong Shen received his B.E. and M.E. degrees from the Department of Electronic Engineering, Tsinghua University, China. He received the Ph.D. degree from the Electrical Engineering Department, University of California Los Angeles (UCLA). He is currently an Associate Professor of the Electrical and Computer Engineering Department at University of Virginia (UVa). Prior to joining UVa, He was a professor in the School of Information Science and Technology at University of Science and Technology of China (USTC). He also has extensive industry experience, having worked for Qualcomm Research, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai, in various full time and consulting roles. His general research interests are in the area of machine learning and wireless communications. In particular, his current research focuses on multi-armed bandits, reinforcement learning, in-context learning, distributed learning, and their applications in wireless communications and networking. He received the NSF CAREER award in 2022. He was the recipient of the Best Paper Award in 2021 IEEE International Conference on Communications (ICC), and the Excellent Paper Award in the 9th International Conference on Ubiquitous and Future Networks (ICUFN 2017). Currently, he serves as an editor for IEEE Transactions on Communications, IEEE Transactions on Wireless Communications IEEE Transactions on Green Communications and Networking, and IEEE Transactions on Machine Learning in Communications and Networking. He previously served as an editor for IEEE Transactions on Wireless Communications in 2016–2021, and IEEE Wireless Communications Letters in 2018–2023. He was the TPC co-chair of the Wireless Communications Symposium of IEEE Globecom 2021, and actively serves as (senior) program committee members/reviewers for Conference on Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR), International Conference on Artificial Intelligence and Statistics (AISTATS), International Joint Conference on Artificial Intelligence (IJCAI), and AAAI Conference on Artificial Intelligence (AAAI). He is a founding member of SpectrumX, an NSF Spectrum Innovation Center.

Performance Period: 10/01/2023 - 09/30/2026

Institution: University of Virginia Main Campus

Award Number: 2313110