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）模型训练转移到边缘，分布式和分散的ML对于支持许多应用程序变得越来越重要，例如自动驾驶，推荐系统和物联网（IoT）。这一趋势对底层通信设计提出了巨大的挑战，并催化其从连接人、连接物到连接智能的演变。CAREER项目开发基础通信技术，使下一代无线系统中的分布式和分散式机器学习成为可能。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机器学习之间建立协同作用。通过与业界合作，该项目的成果可以原型化并集成到实际系统中，这可能会影响6G标准化和其他未来的通信系统。通过将机器学习和人工智能整合到通信和网络课程中，该项目的跨学科性质自然转化为案例研究和一些本科和研究生课程的新发展。教育和外展活动将共同促进为不同群体的聪明年轻人发展成为未来的科学家和工程师提供最佳机会的共同思路。该项目旨在为分布式和去中心化机器学习发展理论基础和新的通信算法，从而促进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，将物理层通信与机器学习紧密结合。此外，本研究将研究衰落和噪声信道对机器学习性能的影响，并设计通信方法以提高机器学习任务的准确性和收敛性。最后，将研究分布式和分散多武装强盗的一种新的自适应通信方法，其中将研究具有对抗性通信的在线学习的编码和交错设计。拟议的研究促进了对分布式和去中心化ML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。该奖项反映了美国国家科学基金会的法定使命，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.