3 citations · 5 across the 10 of their papers we have counts for
10 papers
Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks
Yan Zhu, Jingyang Zhu, Ting Wang +3
Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tas…
Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks
Peng Yang, Ting Wang, Haibin Cai +3
Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster m…
Federated Linear Bandit Learning via Over-the-Air Computation
Jiali Wang, Yuning Jiang, Xin Liu +2
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the envi…
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
Junkai Qian, Yuning Jiang, Xin Liu +4
With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies h…
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
Jiali Wang, Yijie Mao, Ting Wang +1
In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device ad…
Reconfigurable Intelligent Surfaces Empowered Green Wireless Networks with User Admission Control
Jinglian He, Yijie Mao, Yong Zhou +2
Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and energy-efficient technique for 6G. By adjusting the phase shifts of passive reflecting elements, RIS is…
Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks
Xiao Du, Ting Wang, Qiang Feng +4
The 6G network enables a subnetwork-wide evolution, resulting in a "network of subnetworks". However, due to the dynamic mobility of wireless subnetworks, the data transmission of…
Over-the-Air Federated Learning via Second-Order Optimization
Peng Yang, Yuning Jiang, Ting Wang +3
Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and…
Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation
Shaoming Huang, Yong Zhou, Ting Wang +1
Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guaran…
UAV-Assisted Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance tra…