CCSS: Collaborative Research: Towards a Resource Rationing Framework for Wireless Federated Learning

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Federated learning (FL) is an emerging distributed machine learning paradigm that has many attractive properties. Despite the early studies that have demonstrated the potential of jointly optimizing communication and computation, existing designs are not tailored to the unique characteristics of FL. This project aims at developing a novel and rigorous resource allocation framework for wireless FL, which we term resource rationing to emphasize balancing resources over time so that the long-term impact to the final learning outcome is explicitly captured. Resource rationing is built on a rigorous theoretical foundation and guides the algorithmic development that solves specific resource allocation problems in both physical and Media Access Control (MAC) layers. Federated learning is an emerging new application for wireless communications, and this project has potential to advance the technology development of this new use case. Meanwhile, the theoretical foundation, algorithms, and validation will broadly advance the state of the art in machine learning, communication theory, and wireless networking. Developing such practical and impactful technology would also help maintain the leadership of the United States in wireless technologies as well as keep the pipeline to supply high-quality, well-trained, and innovative engineers.The project pursues synergistic activities for the successful design and implementation of resource rationing for wireless FL. Novel convergence analysis of FL with varying resource in each learning round is carried out, which establishes the general later-is-better principle. Guided by the theoretical foundation, the project further builds a comprehensive algorithmic framework for specific resource rationing designs, ranging from physical layer bit loading and adaptive coding and modulation to the MAC layer client selection, bandwidth allocation, and power control.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.