119 citations · 366 across the 21 of their papers we have counts for
6 papers · 1 filter
Federated Nonconvex Sparse Learning
Qianqian Tong, Guannan Liang, Tan Zhu +1
Nonconvex sparse learning plays an essential role in many areas, such as signal processing and deep network compression. Iterative hard thresholding (IHT) methods are the state-of-…
Asynchronous Parallel Stochastic Quasi-Newton Methods
Qianqian Tong, Guannan Liang, Xingyu Cai +2
Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-…
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…
Effective Federated Adaptive Gradient Methods with Non-IID Decentralized Data
Qianqian Tong, Guannan Liang, Jinbo Bi
Federated learning allows loads of edge computing devices to collaboratively learn a global model without data sharing. The analysis with partial device participation under non-IID…
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…
Against Membership Inference Attack: Pruning is All You Need
Yijue Wang, Chenghong Wang, Zigeng Wang +5
The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularit…