Zobrazeno 1 - 10
of 678
pro vyhledávání: '"Zhu, Liehuang"'
Federated learning allows several clients to train one machine learning model jointly without sharing private data, providing privacy protection. However, traditional federated learning is vulnerable to poisoning attacks, which can not only decrease
Externí odkaz:
http://arxiv.org/abs/2406.01080
Recent booming development of Generative Artificial Intelligence (GenAI) has facilitated an emerging model commercialization for the purpose of reinforcement on model performance, such as licensing or trading Deep Neural Network (DNN) models. However
Externí odkaz:
http://arxiv.org/abs/2405.04108
Segmentation of organs of interest in medical CT images is beneficial for diagnosis of diseases. Though recent methods based on Fully Convolutional Neural Networks (F-CNNs) have shown success in many segmentation tasks, fusing features from images wi
Externí odkaz:
http://arxiv.org/abs/2405.04064
Latent Diffusion Models (LDMs) enable a wide range of applications but raise ethical concerns regarding illegal utilization.Adding watermarks to generative model outputs is a vital technique employed for copyright tracking and mitigating potential ri
Externí odkaz:
http://arxiv.org/abs/2405.02696
Large Language Models (LLMs) have revolutionized open-domain dialogue agents but encounter challenges in multi-character role-playing (MCRP) scenarios. To address the issue, we present Neeko, an innovative framework designed for efficient multiple ch
Externí odkaz:
http://arxiv.org/abs/2402.13717
Visible-infrared person re-identification (VIReID) primarily deals with matching identities across person images from different modalities. Due to the modality gap between visible and infrared images, cross-modality identity matching poses significan
Externí odkaz:
http://arxiv.org/abs/2401.05806
Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers. Despite the cost reduction benefits of distributed machine learning, the dissemination of final model weights often le
Externí odkaz:
http://arxiv.org/abs/2401.05895
Blockchain smart contracts have emerged as a transformative force in the digital realm, spawning a diverse range of compelling applications. Since solidity smart contracts across various domains manage trillions of dollars in virtual coins, they beco
Externí odkaz:
http://arxiv.org/abs/2310.20212
With the increasing adoption of smart contracts, ensuring their security has become a critical concern. Numerous vulnerabilities and attacks have been identified and exploited, resulting in significant financial losses. In response, researchers have
Externí odkaz:
http://arxiv.org/abs/2311.00270
Autor:
Xing, Zhibo, Zhang, Zijian, Liu, Jiamou, Zhang, Ziang, Li, Meng, Zhu, Liehuang, Russello, Giovanni
With the rapid advancement of artificial intelligence technology, the usage of machine learning models is gradually becoming part of our daily lives. High-quality models rely not only on efficient optimization algorithms but also on the training and
Externí odkaz:
http://arxiv.org/abs/2310.14848