Search Results for author: Minghui Xu

Found 8 papers, 2 papers with code

VMID: A Multimodal Fusion LLM Framework for Detecting and Identifying Misinformation of Short Videos

no code implementations15 Nov 2024 Weihao Zhong, Yinhao Xiao, Minghui Xu, Xiuzhen Cheng

Short video platforms have become important channels for news dissemination, offering a highly engaging and immediate way for users to access current events and share information.

Fake News Detection Large Language Model +1

Modeling Unified Semantic Discourse Structure for High-quality Headline Generation

no code implementations23 Mar 2024 Minghui Xu, Hao Fei, Fei Li, Shengqiong Wu, Rui Sun, Chong Teng, Donghong Ji

To consolidate the efficacy of S3 graphs, we further devise a hierarchical structure pruning mechanism to dynamically screen the redundant and nonessential nodes within the graph.

Abstract Meaning Representation Headline Generation +1

DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

1 code implementation31 Oct 2023 Xinwei Wu, Junzhuo Li, Minghui Xu, Weilong Dong, Shuangzhi Wu, Chao Bian, Deyi Xiong

The ability of data memorization and regurgitation in pretrained language models, revealed in previous studies, brings the risk of data leakage.

Memorization Model Editing

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-based Federated Learning

no code implementations18 Feb 2022 Zhilin Wang, Qin Hu, Ruinian Li, Minghui Xu, Zehui Xiong

Since each client has a limited amount of computing resources, the problem of allocating computing resources into training and mining needs to be carefully addressed.

Federated Learning

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

no code implementations16 Oct 2021 Qin Hu, Zhilin Wang, Minghui Xu, Xiuzhen Cheng

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost.

Federated Learning Privacy Preserving

A Systematic Survey of Blockchained Federated Learning

no code implementations5 Oct 2021 Zhilin Wang, Qin Hu, Minghui Xu, Yan Zhuang, Yawei Wang, Xiuzhen Cheng

Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL.

BIG-bench Machine Learning Federated Learning +1

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