most citedFederated Learning with Differential Privacy: Algorithms and Performance Analysis

88 citations · 156 across the 13 of their papers we have counts for

collaborators

16 papers

cs.LG202012 cited

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

Chuan Ma, Jun Li, Ming Ding +4

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without…

eess.SP20201 cited

Binary Representaion for Non-binary LDPC Code with Decoder Design

Yang Yu, Wen Chen, Jun Li +2

The equivalent binary parity check matrices for the binary images of the cycle-free non-binary LDPC codes have numerous bit-level cycles. In this paper, we show how to transform th…

cs.LG202029 cited

Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

Jun Li, Li Fuxin, Sinisa Todorovic

Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, w…

cs.LG2019

Lifelong Spectral Clustering

Gan Sun, Yang Cong, Qianqian Wang +2

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a f…

cs.CV20195 cited

LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution

Bin Sun, Jun Li, Ming Shao +1

Deep learning has become popular in recent years primarily due to the powerful computing device such as GPUs. However, deploying these deep models to end-user devices, smart phones…

cs.LG201988 cited

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

Kang Wei, Jun Li, Ming Ding +6

In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to t…