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

随着新兴的范式转向将数据收集和机器学习(ML)模型培训转移到边缘，分布式和去中心化的ML对于支持许多应用程序(如自动驾驶、推荐系统和物联网(IoT))变得越来越关键。这一趋势对底层通信设计提出了严峻的挑战，并催化了它从连接人和连接事物到连接智能的演变。这个职业项目开发基础通信技术，以便在下一代无线系统中实现分布式和去中心化ML。它将无线通信从纯数据传输转变为智能传输，以紧密结合的方式在通信和ML之间建立了协同。在与业界的合作下，该项目实现的成果可以进行原型制作并集成到实际系统中，可能会影响6G标准化和其他未来的通信系统。这个项目的跨学科性质自然转化为案例研究和一些本科生和研究生水平课程的新发展，通过将ML和人工智能整合到通信和网络课程中。教育和外展活动将共同促进一条共同的主线，即为不同的聪明青年群体提供最佳机会，以发展成为未来的科学家和工程师。该项目旨在为分布式和分散化的ML开发理论基础和新颖的通信算法，从而催化无线通信向连接智能的范式转变。为此，本项目将开发一种新的随机正交化原理，将物理层通信与ML紧密结合。此外，还将研究衰落和噪声信道对ML性能的影响，并设计通信方法来提高ML任务的精度和收敛。最后，研究了一种新的分布式和分散式多武装匪徒自适应通信方法，其中将研究在线学习的对抗性通信编码和交织设计。这项拟议的研究促进了对分布式和分散式ML与通信之间的协同作用的基本理解，并将在本项目研究的具体问题之外具有广泛的应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.