MLWiNS: Decentralized Heterogeneous Deep Learning for Efficient Wireless Spectrum Monitoring

Project Overview

As wireless networks evolve to be increasingly massive and complex, traditional spectrum monitoring methods with model-based signal processing techniques have become inadequate and may even fail to provide accurate wireless network evaluation. Meanwhile, deep learning techniques have been proven successful in standard centralized learning tasks (e.g., image classification), yet it is barely explored for large-scale wireless sensing systems, which entail unconventional node distribution, complex channel fading, and user collaboration opportunities. This project develops innovative decentralized heterogeneous deep learning techniques for large-scale wireless systems. The outcomes of this project lead to technical innovations that tackle several major challenges of the state-of-the-art wireless sensing and management systems, including the incapability of conventional sensing and management schemes in ultra-wide wireless spectrum settings, the difficulty in handling heterogeneous tasks, and non-IID data with deep learning technologies, as well as the costly overhead of communication and computation in distributed deep learning for large-scale networks.

This project addresses the unique challenges of large-scale wireless spectrum sensing by developing a revolutionary decentralized deep learning framework. Three main thrusts are planned. In Thrust 1, major challenges of complex and large-scale wireless spectrum sensing nowadays are investigated, and an innovative deep learning-based solution is developed for practical spectrum sensing tasks. In Thrust 2, dedicated communication and computation schemes are developed to optimize the performance of the proposed decentralized deep learning framework. In Thrust 3, the very first exploratory effort is made to understand and utilize the intricate role of machine learning in spectrum management, based on the key observation that it consumes wireless network resources to bring in added value to network resource utilization. Experimental testing is demonstrated for practical spectrum monitoring applications. The proposed wireless sensing and management system can benefit a plethora of large-scale wireless network systems, such as a 5G wireless network and other large-scale mesh networking systems. The education plan enhances existing curricula and pedagogy by integrating interdisciplinary modules on embedded systems, mobile computing, and machine learning with newly developed teaching practices.

Project Personnel

Principal Investigators

Xiang Chen
George Mason University

Zhi (Gerry) Tian
George Mason University


Jinjun Xiong
IBM Thomas J. Watson Research Center

Di Wang
Microsoft Research

Lingjia Liu
Virginia Tech

Project Products

Conference Papers

[KDD ‘21] Fed2: Feature-Aligned Federated Learning
F. Yu, W. Zhang, Z. Qin, Z. Xu, D. Wang, C. Liu, Z. Tian, and X. Chen.
The 27th ACM SigKDD Conference on Knowledge Discovery and Data Mining, Nov. 2021.

[DAC ‘21] Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
Z. Xu, F. Yu, J. Xiong, and X. Chen.
The 58th Design Automation Conference, Dec. 2021.

[SEC ‘20] Exploring the Design Space of Efficient Deep Neural Networks
Z. Qin, and X. Chen.
The 5th ACM/IEEE Symposium on Edge Computing, Nov. 2020.

Journal Papers

[TVT] Deep Learning-Assisted Energy-Efficient Task Offloading in Vehicular Edge Computing Systems
B. Shang, L. Liu, and Z. Tian.
IEEE Transactions on Vehicular Technology, Jun. 2021.

[TNNLS] DQC-ADMM: Decentralized Dynamic ADMM with Quantized and Censored Communications
Y. Liu, G. Wu, Z. Tian, and Q. Ling.
IEEE Transactions on Neural Networks and Learning Systems, Jan. 2021.

Code Repository


IF-Lab Repository on Github

Broadening Participation Activities

Industry Collaboration

IBM Research Lab
Microsoft Research

Education Projects

Aspiring Scientists Summer Internship Program