Search Results for author: Qin Hu

Found 8 papers, 0 papers with code

Can We Trust the Similarity Measurement in Federated Learning?

no code implementations20 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.

Federated Learning Model Poisoning

A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning

no code implementations23 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).

Hand Gesture Recognition Hand-Gesture Recognition +1

Pit-Pattern Classification of Colorectal Cancer Polyps Using a Hyper Sensitive Vision-Based Tactile Sensor and Dilated Residual Networks

no code implementations13 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.

Transfer Learning

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

Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning

no code implementations15 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.

Edge-computing Fairness

Blockchain-based Federated Learning: A Comprehensive Survey

no code implementations5 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.

BIG-bench Machine Learning Federated Learning

Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm

no code implementations26 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

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