Collaborative Research: CNS Core: Medium: Towards Federated Learning over 5G Mobile Devices: High Efficiency, Low Latency, and Good Privacy

Project Information

Project Synopsis

 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.

Personnel and Collaborators

Personnel at UH

  • Dr. Miao Pan, PI
  • Dr. Xin Fu, Co-PI
  • Ms. Rui Chen, PhD Candidate
  • Mr. Jiahao Ding, PhD Student (Graduated in May, 2022)
  • Ms. Pavana Prakash, PhD Student
  • Mr. Qiyu Wan, PhD Candidate
  • Mr. Lening Wang, PhD Student

Personnel at UF

  • Dr. Yuguang Fang, PI
  • Mr. Guangyu Zhu, PhD Student

Personnel at UTSA

  • Dr. Yanmin Gong, PI
  • Dr. Yuanxiong Guo, Co-PI
  • Mr. Zhidong Gao, PhD Student
  • Mr. Zhenxiao Zhang, PhD Student

Research Progress and Outreach Activities


  1. Jeffrey Jiarui Chen, Rui Chen, Xinyue Zhang, and Miao Pan, “A Privacy Preserving Federated Learning Framework for COVID-19 Vulnerability Map Construction”, IEEE International Conference on Communications (ICC’21), Virtual/Montreal, Canada, June 14-23, 2021.
  2. Rui Chen, Liang Li, Jeffrey Jiarui Chen, Ronghui Hou, Yanmin Gong, Yuanxiong Guo, and Miao Pan, “COVID-19 Vulnerability Map Construction via Location Privacy Preserving Mobile Crowdsourcing“, IEEE Global Communications Conference (GLOBECOM’20), Taipei, Taiwan, December 7-11, 2020.
  3. Hao Gao, Wuchen Li, Miao Pan, Zhu Han, and H. Vincent Poor, “Analyzing Social Distancing and Seasonality of COVID-19 with Mean Field Evolutionary Dynamics”, IEEE Global Communications Conference (GLOBECOM’20) Special Workshop on Communications and Networking Technologies for Responding to COVID-19, Taipei, Taiwan, December 7-11, 2020.

Links to Code Repositories

  1. Github Code Links: