16 citations · 64 across the 21 of their papers we have counts for
25 papers
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Yuhan Yang, Yong Zhou, Youlong Wu +1
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It…
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…
Sparse Signal Processing for Massive Connectivity via Mixed-Integer Programming
Shuang Liang, Yuanming Shi, Yong Zhou
Massive connectivity is a critical challenge of Internet of Things (IoT) networks. In this paper, we consider the grant-free uplink transmission of an IoT network with a multi-ante…
Over-the-Air Computation via Cloud Radio Access Networks
Lukuan Xing, Yong Zhou, Yuanming Shi
Over-the-air computation (AirComp) has recently been recognized as a promising scheme for a fusion center to achieve fast distributed data aggregation in wireless networks via expl…
Over-the-Air Decentralized Federated Learning
Yandong Shi, Yong Zhou, Yuanming Shi
In this paper, we consider decentralized federated learning (FL) over wireless networks, where over-the-air computation (AirComp) is adopted to facilitate the local model consensus…
UAV Aided Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels. Different from…