NSF Award
2114283
Federated learning (FL) is revolutionizing machine learning by catalyzing a paradigm shift from cloud-based centralized learning towards distributed, on-device edge learning. FL enables devices to collaboratively train and execute a global learning task by using local processing and simple learning parameters exchange, thus avoiding the communication and privacy concerns associated with sharing large data volumes with a remote cloud. Owing to its attractive privacy, scalability, and communication features, FL will be an integral edge component of Internet of Things (IoT) services such as autonomous systems. However, when deployed over the wireless IoT edge, the performance of FL will be largely constrained by the quality of the wireless links used to exchange the local and global FL model parameters. Since the next-generation IoT will be powered by a wireless cellular system (e.g., 5G), reaping the benefits of FL for the IoT hinges on understanding how wireless factors, such as fading, interference, and delay, impact the convergence and performance of FL (e.g., accuracy, reliability, and convergence time). The goal of this research is to develop a foundational framework that rigorously answers fundamental questions on the achievable FL performance over realistic, large-scale wireless edge networks thus facilitating FL integration unto a real-world IoT. The research is coupled with a well-crafted educational plan that includes a new course at the intersection of communications and machine learning as well as a significant involvement of graduate and undergraduate students at all levels. Broad dissemination and outreach will be ensured via several workshops, tutorials, outreach events, and other tools.<br/><br/>This research will develop a novel, holistic framework for performance analysis and optimization of FL over large-scale wireless cellular edge networks. The proposed framework will yield major innovations across both wireless and FL fields: 1) A scalable hierarchical wireless architecture that allows a large-scale implementation of FL over wireless cellular systems, 2) Rigorous performance analysis of hierarchical FL over wireless edge networks that will yield novel FL performance metrics that jointly couple learning performance indicators, such as training accuracy and convergence time, 3) Novel notions of reliability for FL over wireless networks to enable the operation of FL under extreme network conditions and in presence of IoT device mobility and spatio-temporal correlations, and 4) Suitable resource allocation algorithms that can optimize the performance of hierarchical FL over wireless edge networks. The results will be validated using various simulation and experimental means.<br/><br/>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.
