7 papers
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Yangjing Wang, Ouya Wang, Shenglong Zhou +1
The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studi…
Decentralized Federated Learning by Partial Message Exchange
Shan Sha, Shenglong Zhou, Xin Wang +2
Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks. However, it…
Preconditioned Inexact Stochastic ADMM for Deep Model
Shenglong Zhou, Ouya Wang, Ziyan Luo +2
Deep learning models are usually trained with stochastic gradient descent-based algorithms, but these optimizers face inherent limitations, such as slow convergence and stringent a…
Neural Collapse based Deep Supervised Federated Learning for Signal Detection in OFDM Systems
Kaidi Xu, Shenglong Zhou, Geoffrey Ye Li
Future wireless networks are expected to be AI-empowered, making their performance highly dependent on the quality of training datasets. However, physical-layer entities often obse…
BADM: Batch ADMM for Deep Learning
Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Stochastic gradient descent-based algorithms are widely used for training deep neural networks but often suffer from slow convergence. To address the challenge, we leverage the fra…
Fast Adaptation for Deep Learning-based Wireless Communications
Ouya Wang, Hengtao He, Shenglong Zhou +4
The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges h…