18 citations · 27 across the 5 of their papers we have counts for
6 papers · 1 filter
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
Tian Liu, Jiahao Ding, Ting Wang +2
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) des…
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices
Pavana Prakash, Jiahao Ding, Maoqiang Wu +3
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. In…
Evaluation of Inference Attack Models for Deep Learning on Medical Data
Maoqiang Wu, Xinyue Zhang, Jiahao Ding +4
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained f…
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning
Guannan Liang, Qianqian Tong, Jiahao Ding +2
Sparse learning is a very important tool for mining useful information and patterns from high dimensional data. Non-convex non-smooth regularized learning problems play essential r…
Towards Plausible Differentially Private ADMM Based Distributed Machine Learning
Jiahao Ding, Jingyi Wang, Guannan Liang +2
The Alternating Direction Method of Multipliers (ADMM) and its distributed version have been widely used in machine learning. In the iterations of ADMM, model updates using local p…
Differentially Private ADMM for Distributed Medical Machine Learning
Jiahao Ding, Xiaoqi Qin, Wenjun Xu +3
Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multip…