no code implementations • 20 Oct 2023 • Zhilin Wang, Qin Hu, Xukai Zou
We first uncover the deficiencies of similarity metrics that high-dimensional local models, including benign and poisoned models, may be evaluated to have the same similarity while being significantly different in the parameter values.
no code implementations • 23 Sep 2023 • Golara Ahmadi Azar, Qin Hu, Melika Emami, Alyson Fletcher, Sundeep Rangan, S. Farokh Atashzar
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG).
no code implementations • 13 Nov 2022 • Nethra Venkatayogi, Qin Hu, Ozdemir Can Kara, Tarunraj G. Mohanraj, S. Farokh Atashzar, Farshid Alambeigi
In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet architecture, and the transfer learning concept applied on a small dataset with the scale of hundreds of images.
no code implementations • 18 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.
no code implementations • 16 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.
no code implementations • 15 Oct 2021 • Qin Hu, Shengling Wang, Zeihui Xiong, Xiuzhen Cheng
In particular, federated edge learning (FEL) becomes prominent in securing the privacy of data owners by keeping the data locally used to train ML models.
no code implementations • 5 Oct 2021 • Zhilin Wang, Qin Hu
Then, we analyze the concrete functions of BCFL from the perspective of mechanism design and illustrate what problems blockchain addresses specifically for FL.
no code implementations • 26 Dec 2019 • Xidi Qu, Shengling Wang, Qin Hu, Xiuzhen Cheng
However, the separation between the data usufruct and ownership in Blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning.
Cryptography and Security