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

随着数据收集和机器学习（ML）模型训练向边缘转移的新范式转变，分布式和分散式ML对于增强许多应用程序（如自动驾驶，推荐系统和物联网（IoT））变得越来越重要。这一趋势对底层的通信设计提出了巨大的挑战，并促进了其从连接人和连接物到连接智能的演变。这个CAREER项目开发基础通信技术，以在下一代无线系统中实现分布式和分散式ML。它将无线通信从纯粹的数据传输转变为智能传输，以紧密集成的方式在通信和ML之间建立协同作用。通过与行业合作，该项目实现的成果可以原型化并集成到真实的系统中，这可能会影响6G标准化和其他未来的通信系统。该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课程的新发展，通过将ML和AI整合到通信和网络课程中。教育和外展活动将共同促进一个共同点，即为不同群体的聪明年轻人提供发展成为未来科学家和工程师的最佳机会。该项目旨在开发分布式和去中心化ML的理论基础和新型通信算法，从而催化无线通信向连接智能的范式转变。为此，该项目将开发一种新的随机正交化原理，将物理层通信与ML紧密集成。此外，研究衰落和噪声信道对ML性能的影响，并设计通信方法以提高ML任务的准确性和收敛性。最后，一种新的自适应通信方法的分布式和分散的多武装土匪将研究，编码和交织设计的在线学习与对抗通信。该研究将促进对分布式和去中心化机器学习与通信之间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知识价值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.