CAREER: Towards a Communication Foundation for Distributed and Decentralized Machine Learning

University Of Virginia Main Campus, Charlottesville VA

Investigators

Abstract

With the emerging paradigm shift towards moving the data collection and machine learning (ML) model training to the edge, distributed and decentralized ML has become increasingly critical to empowering many applications, such as autonomous driving, recommender systems, and Internet of Things (IoT). This trend imposes formidable challenges on the underlying communication design and catalyzes its evolution from connecting people and connecting things to connecting intelligence. This CAREER project develops fundamental communication technologies to enable distributed and decentralized ML in next-generation wireless systems. It transforms wireless communications from pure data transfer to intelligence transfer, building a synergy between communications and ML in a closely integrated fashion. In partnership with industry, results enabled by this project can be prototyped and integrated into real systems, potentially impacting 6G standardization and other future communication systems. The cross disciplinary nature of this project naturally translates into case studies and new development in a number of undergraduate and graduate level courses, by integrating ML and AI to the curriculum of communications and networking. The education and outreach activities will collectively promote a common thread of providing the best opportunities for diverse groups of bright young minds to develop into future scientists and engineers. This project aims at developing the theoretical foundation and novel communication algorithms for distributed and decentralized ML, thereby catalyzing a paradigm shift of wireless communications towards connecting intelligence. Towards this end, this project will develop a novel random orthogonalization principle that tightly integrates physical layer communications with ML. Additionally, the research will study the impact of fading and noisy channels on the performance of ML, and design communication methods to improve the accuracy and convergence of the ML tasks. Finally, a novel adaptive communication method for distributed and decentralized multi-armed bandits will be investigated, where coding and interleaving designs for online learning with adversarial communications will be studied. The proposed research promotes the fundamental understanding of the synergy between distributed and decentralized ML and communications, and will have broad applications beyond the specific problems studied in this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.