70 citations · 85 across the 4 of their papers we have counts for
7 papers
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu +1
Federated Learning (FL) framework brings privacy benefits to distributed learning systems by allowing multiple clients to participate in a learning task under the coordination of a…
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates
Zhuohang Li, Luyang Liu, Jiaxin Zhang +1
Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the clie…
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
Yong Dai, Jian Liu, Jian Zhang +2
Sentiment analysis (SA) is an important research area in cognitive computation-thus in-depth studies of patterns of sentiment analysis are necessary. At present, rich resource data…
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Yong Dai, Jian Liu, Xiancong Ren +1
Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target…
Free-riders in Federated Learning: Attacks and Defenses
Jierui Lin, Min Du, Jian Liu
Federated learning is a recently proposed paradigm that enables multiple clients to collaboratively train a joint model. It allows clients to train models locally, and leverages th…
Latent Dirichlet Allocation in Generative Adversarial Networks
Lili Pan, Shen Cheng, Jian Liu +2
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the un…