no code implementations • 26 Feb 2024 • Jiahao Wang, Sikun Yang, Heinz Koeppl, Xiuzhen Cheng, Pengfei Hu, Guoming Zhang
Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data.
no code implementations • 19 Jan 2024 • Youming Tao, Cheng-Long Wang, Miao Pan, Dongxiao Yu, Xiuzhen Cheng, Di Wang
We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data.
no code implementations • 15 Jan 2024 • Biwei Yan, Hongliang Zhang, Minghui Xu, Dongxiao Yu, Xiuzhen Cheng
Federated learning is a powerful technique that enables collaborative learning among different clients.
no code implementations • 18 Mar 2023 • Youming Tao, Sijia Cui, Wenlu Xu, Haofei Yin, Dongxiao Yu, Weifa Liang, Xiuzhen Cheng
To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously.
1 code implementation • 4 Mar 2023 • Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
1 code implementation • 4 Jan 2023 • Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
1 code implementation • 24 Nov 2022 • Huanle Zhang, Lei Fu, Mi Zhang, Pengfei Hu, Xiuzhen Cheng, Prasant Mohapatra, Xin Liu
In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training.
no code implementations • 29 Jan 2022 • Feng Li, Xuyang Yuan, Lina Wang, Huan Yang, Dongxiao Yu, Weifeng Lv, Xiuzhen Cheng
The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.
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.
1 code implementation • 18 May 2021 • Zekai Chen, Xiao Zhang, Xiuzhen Cheng
On the one hand, multiple views across tasks possibly relate to each other under practical situations.
1 code implementation • 18 May 2021 • Zekai Chen, Fangtian Zhong, Zhumin Chen, Xiao Zhang, Robert Pless, Xiuzhen Cheng
Prior studies in predicting user response leveraged the feature interactions by enhancing feature vectors with products of features to model second-order or high-order cross features, either explicitly or implicitly.
1 code implementation • 8 Apr 2021 • Zekai Chen, Dingshuo Chen, Xiao Zhang, Zixuan Yuan, Xiuzhen Cheng
This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture.
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
no code implementations • 22 Oct 2019 • Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.