6 citations · 7 across the 5 of their papers we have counts for
6 papers
Secure Joint Source-Channel Coding for the AWGN Channel with Feedback: A Finite Blocklength Analysis
Sheng Su, Yuhan Yang, Chao Qi +3
In the literature, it has been shown that the secrecy capacity of the additive white Gaussian noise (AWGN) wiretap channel with noise-free feedback equals the capacity of the same…
Coding for Fading Channels with Imperfect CSI at the Transmitter and Quantized Feedback
Yuhan Yang, Haoheng Yuan, Chao Qi +2
The classical Schalkwijk-Kailath (SK) scheme for the additive Gaussian noise channel with noiseless feedback is highly efficient since its coding complexity is extremely low and th…
Coding for Quasi-Static Fading Channel with Imperfect CSI at the Transmitter and Quantized Feedback
Yuhan Yang, Mei Han, Haonan Zhang +3
The classical Schalkwijk-Kailath (SK) scheme for the additive Gaussian noise channel with noiseless feedback is highly efficient since its coding complexity is extremely low and th…
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Yuhan Yang, Youlong Wu, Yuning Jiang +1
Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous…
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…
Fast Convergence Algorithm for Analog Federated Learning
Shuhao Xia, Jingyang Zhu, Yuhan Yang +3
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end d…