NSF CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Networking

Project Information

CAREER: Ubiquitous and Time-Critical Federated Learning with Cooperative Mobile Edge Networking, link, (CNS-2047761), Oct. 1, 2021 – Sep. 30, 2026.

Synopsis

This project develops a novel Federated learning (FL) framework based on cooperative mobile edge networking that can efficiently support learning and decision making on distributed Internet-of-Things (IoT) data with high accuracy, low latency, and guaranteed privacy. Three interconnected research thrusts are investigated in this project: 1) design of novel network-aware learning algorithms under a two-level network structure to ensure efficient and effective model training from decentralized data on IoT devices over wireless edge networks; 2) jointly optimize resource allocation and learning based on deep reinforcement learning to learn an accurate model rapidly under system heterogeneity and resource constraints; 3) develop novel differential privacy techniques to rigorously protect the privacy of personal data on IoT devices while maintaining high model accuracy and reducing communication cost. The proposed research will enable next-generation wireless edge networks that support a plethora of delay-sensitive and data-driven IoT applications. The proposed research will benefit not only the wireless networking but also machine learning research communities by bridging the gap between the evolving mobile computing and networking technologies and rapidly advancing machine learning techniques.

Personnel

  • Rui Hu, PhD student (graduated)
  • Zhidong Gao, PhD student
  • Zhenxiao Zhang, PhD student
  • Yu Zhang, PhD student

Publications

  • Y Guo, Y Sun, R Hu, and Y Gong, “Hybrid Local SGD for Federated Learning with Heterogeneous Communications,” The International Conference on Learning Representations (ICLR), Virtual, April 25-29, 2022.

  • R Hu, Y Guo, and Y Gong, “Concentrated differentially private federated learning with performance analysis,” IEEE Open Journal of the Computer Society, vol. 2, pp. 276–289, 2021.

  • R Hu, Y Guo, and Y Gong, “Energy-efficient distributed machine learning at wireless edge with device- to-device communication,” in ICC 2022 - 2022 IEEE International Conference on Communications (ICC), 2022, pp. 1–6.

  • Z Zhang, Z Gao, Y Guo, Y Gong, “Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking,” IEEE Transactions on Mobile Computing (TMC).

  • T. Wang, Y. Du, Y. Gong, K. Choo, Y. Guo, “Applications of Federated Learning in Mobile Health: Scoping Review”, JOURNAL OF MEDICAL INTERNET RESEARCH.