1 code implementation • 4 Jan 2023 • Li Zhang, Jiahao Ding, Yifeng Gao, Jessica Lin
During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner.
no code implementations • 29 Jan 2022 • Tian Liu, Jiahao Ding, Ting Wang, Miao Pan, Mingsong Chen
However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure.
no code implementations • 1 Nov 2021 • Pavana Prakash, Jiahao Ding, Maoqiang Wu, Minglei Shu, Rong Yu, Miao Pan
Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices.
no code implementations • 31 Oct 2020 • Maoqiang Wu, Xinyue Zhang, Jiahao Ding, Hien Nguyen, Rong Yu, Miao Pan, Stephen T. Wong
This paper aims to attract interest from researchers in the medical deep learning community to this important problem.
no code implementations • 22 Oct 2020 • Di Wang, Jiahao Ding, Lijie Hu, Zejun Xie, Miao Pan, Jinhui Xu
To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees.
no code implementations • 14 Sep 2020 • Guannan Liang, Qianqian Tong, Jiahao Ding, Miao Pan, Jinbo Bi
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data.
no code implementations • 11 Aug 2020 • Jiahao Ding, Jingyi Wang, Guannan Liang, Jinbo Bi, Miao Pan
In PP-ADMM, each agent approximately solves a perturbed optimization problem that is formulated from its local private data in an iteration, and then perturbs the approximate solution with Gaussian noise to provide the DP guarantee.
no code implementations • 14 Jan 2020 • Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, Miao Pan
In order to enforce $\epsilon$-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint.
no code implementations • 7 Jan 2019 • Jiahao Ding, Xiaoqi Qin, Wenjun Xu, Yanmin Gong, Chi Zhang, Miao Pan
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests.