no code implementations • 29 Dec 2023 • Jie Shen, Shusen Yang, Cong Zhao, Xuebin Ren, Peng Zhao, Yuqian Yang, Qing Han, Shuaijun Wu
Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry.
1 code implementation • ICCV 2023 • Yizhe Li, Yu-Lin Tsai, Xuebin Ren, Chia-Mu Yu, Pin-Yu Chen
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
no code implementations • 2 Mar 2022 • ZiHao Zhou, Yanan Li, Xuebin Ren, Shusen Yang
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data.
no code implementations • 9 Oct 2020 • Fangyuan Zhao, Xuebin Ren, Shusen Yang, Qing Han, Peng Zhao, Xinyu Yang
To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios.
no code implementations • 22 Apr 2020 • Qing Han, Shusen Yang, Xuebin Ren, Cong Zhao, Jingqi Zhang, Xinyu Yang
However, heterogeneous and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of Edge Learning (i. e. edge-cloud collaborative machine learning).
no code implementations • 17 Dec 2019 • Yanan Li, Shusen Yang, Xuebin Ren, Cong Zhao
Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate.
no code implementations • 27 Nov 2019 • Jun Zhao, Teng Wang, Tao Bai, Kwok-Yan Lam, Zhiying Xu, Shuyu Shi, Xuebin Ren, Xinyu Yang, Yang Liu, Han Yu
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
no code implementations • 5 Jun 2019 • Yanan Li, Xuebin Ren, Shusen Yang, Xinyu Yang
Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data.
no code implementations • 4 Jun 2019 • Teng Wang, Jun Zhao, Han Yu, Jinyan Liu, Xinyu Yang, Xuebin Ren, Shuyu Shi
To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision.
no code implementations • 4 Jun 2019 • Fangyuan Zhao, Xuebin Ren, Shusen Yang, Xinyu Yang
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications.