Energizing Federated Learning via Filter-Aware Attention

no code implementations • 18 Nov 2023 • Ziyuan Yang, Zerui Shao, Huijie Huangfu, Hui Yu, Andrew Beng Jin Teoh, Xiaoxiao Li, Hongming Shan, Yi Zhang

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity.

Federated Learning

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

1 code implementation • 9 Nov 2023 • Meiling Fang, Marco Huber, Julian Fierrez, Raghavendra Ramachandra, Naser Damer, Alhasan Alkhaddour, Maksim Kasantcev, Vasiliy Pryadchenko, Ziyuan Yang, Huijie Huangfu, Yingyu Chen, Yi Zhang, Yuchen Pan, Junjun Jiang, Xianming Liu, Xianyun Sun, Caiyong Wang, Xingyu Liu, Zhaohua Chang, Guangzhe Zhao, Juan Tapia, Lazaro Gonzalez-Soler, Carlos Aravena, Daniel Schulz

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023).

Face Presentation Attack Detection valid

Privacy-Preserving Encrypted Low-Dose CT Denoising

no code implementations • 13 Oct 2023 • Ziyuan Yang, Huijie Huangfu, Maosong Ran, Zhiwen Wang, Hui Yu, Yi Zhang

In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage.

Denoising Privacy Preserving

Comprehensive Competition Mechanism in Palmprint Recognition

1 code implementation • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023 • Ziyuan Yang, Huijie Huangfu, Lu Leng, Bob Zhang, Andrew Beng Jin Teoh, Yi Zhang

The traditional competition mechanism focuses solely on selecting the winner of different channels without considering the spatial information of the features.

Robust Split Federated Learning for U-shaped Medical Image Networks

1 code implementation • 13 Dec 2022 • Ziyuan Yang, Yingyu Chen, Huijie Huangfu, Maosong Ran, Hui Wang, Xiaoxiao Li, Yi Zhang

To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL.

Federated Learning