NSF CAREER: Ubiquitous & Time-Critical Federated Learning via Cooperative Mobile Edge Networking
Award: NSF CNS-2047761
Period: Oct 1, 2021 – Sep 30, 2026
Project Overview
We develop a federated learning (FL) framework leveraging cooperative mobile edge networking to support distributed IoT data with high accuracy, low latency, and rigorous privacy guarantees. The project advances:
- Network-aware learning algorithms: Two-level, topology- and channel-aware FL to efficiently train from decentralized on-device data over wireless edge networks.
- Joint optimization of learning & resources: Deep RL–driven client scheduling and resource allocation to achieve rapid, robust convergence under device/network heterogeneity and constraints.
- Privacy & robustness: Differential privacy and attack-resilient methods that protect personal data while maintaining model utility and reducing communication overhead.
These advances bridge wireless networking and modern ML to enable next-generation edge AI systems powering delay-sensitive, data-driven IoT applications.
People
- PI: Yanmin Gong
- Students: Zhidong Gao (PhD, graduated), Zhenxiao Zhang (PhD), Yu Zhang (PhD, graduated), Rui Hu (PhD, graduated)
Selected Publications (2024–2025)
-
PFedSAM: Secure Federated Learning Against Backdoor Attacks via Personalized Sharpness-Aware Minimization
Z. Zhang, Y. Guo, Y. Gong. IEEE ICC, 2025. -
Federated Adaptive Fine-Tuning of Large Language Models with Heterogeneous Quantization and LoRA
Z. Gao, Z. Zhang, Y. Gong, Y. Guo. IEEE INFOCOM, 2025. (Acceptance ratio: 18.65%) -
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
Y. Zhang, Z. Gao, Z. Zhang, T. Wang, Y. Gong, Y. Guo. IEEE Trans. Vehicular Technology (TVT), 2025. -
Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
Z. Zhang, Z. Gao, Y. Guo, Y. Gong. IEEE Trans. Mobile Computing (TMC), 2024. -
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devices
Y. Li, X. Qin, J. Geng, R. Chen, Y. Hou, Y. Gong, M. Pan, P. Zhang. IEEE TMC, 2024. -
Quantum-Assisted Joint Virtual Network Function Deployment and Maximum Flow Routing for Space Information Networks
Y. Zhang, Y. Gong, L. Fan, Y. Wang, Z. Han, Y. Guo. IEEE TMC, 2024. -
Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks
Y. Zhang, Y. Gong, L. Fan, Y. Wang, Z. Han, Y. Guo. IEEE TMC, 2024. -
Quantum-Assisted Joint Caching and Power Allocation for Integrated Satellite-Terrestrial Networks
Y. Zhang, Y. Gong, L. Fan, Y. Wang, Z. Han, Y. Guo. IEEE Trans. Network Science and Engineering (TNSE), 2024. -
DAFL: Device-to-Device Transmissions for Delay-Efficient Federated Learning over Mobile Devices
H. Su, P. Prakkash, R. Chen, Y. Gong, R. Yu, X. Fu, M. Pan. IEEE Internet of Things Journal (IoT-J), 2024.
Earlier Representative Publications
- Hybrid Local SGD for Federated Learning with Heterogeneous Communications
Y. Guo, Y. Sun, R. Hu, Y. Gong. ICLR, 2022. - Concentrated Differentially Private Federated Learning with Performance Analysis
R. Hu, Y. Guo, Y. Gong. IEEE Open Journal of the Computer Society, 2021. - Energy-Efficient Distributed ML at the Wireless Edge with Device-to-Device Communication
R. Hu, Y. Guo, Y. Gong. IEEE ICC, 2022. - Scalable and Low-Latency Federated Learning with Cooperative Mobile Edge Networking
Z. Zhang, Z. Gao, Y. Guo, Y. Gong. IEEE TMC, in press/early access.
News & Updates
- 2025: INFOCOM’25 accepted paper on federated adaptive fine-tuning for LLMs.
- 2025: ICC’25 accepted paper (PFedSAM) on secure FL against backdoor attacks.
- 2024–2025: Multiple journal publications in TMC, TVT, IoT-J, and TNSE.
Contact
- Email: yanmin.gong@tamu.edu
- GitHub: @yanmingong · Google Scholar · ORCID