ABSTRACT

Recent emerging federated learning (FL) allows distributed data sources to collaboratively train a global model without sharing their privacy sensitive raw data. However, due to the huge size of the deep learning model, the model downloads and updates generate significant amount of network traffic which exerts tremendous burden to existing telecommunication infrastructure. This project takes FL over 5G mobile devices as a workable application scenario to address this dilemma, which will significantly improve the design, analysis and implementation of FL over 5G mobile devices. The research outcomes will substantially enrich the knowledge of machine learning technologies and 5G systems and beyond. Moreover, this project is multidisciplinary, involving machine learning/deep learning/federated learning, edge computing, wireless communications and networking, security and privacy, computer architectural design, etc., which will serve as a fruitful training ground for both graduate and undergraduate students to equip them with multidisciplinary skills for future work force to boost the national economy. Furthermore, outreach activities to high school students will increase the participation of female and minority students in science and engineering.

Specifically, by observing that iterative model updates tend to show high sparsity, the investigators leverage model update sparsity to design model pruning and quantization schemes to optimize local training and privacy-preserving model updating in order to lower both energy consumption and model update traffic. They achieve this design goal by conducting the four research tasks: (1) designing software-hardware co-designed model pruning schemes and adaptive quantization techniques in FL within a single 5G mobile device according to the local data and model sparsity property to reduce the local computation and memory access; (2) making sound trade-off between "working" (i.e., local computing) and "talking" (i.e., 5G wireless transmissions) to boost the overall energy/communications efficiency for FL over 5G mobile devices; (3) developing novel differentially private compression schemes based on sparsification property and quantization adaptability to rigorously protect data privacy while maintaining high model accuracy and communication efficiency in FL; and (4) building a testbed to thoroughly evaluate the proposed designs.

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.

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