AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization

no code implementations • 26 Sep 2021 • Qingsong Zhang, Bin Gu, Cheng Deng, Songxiang Gu, Liefeng Bo, Jian Pei, Heng Huang

To address the challenges of communication and computation resource utilization, we propose an asynchronous stochastic quasi-Newton (AsySQN) framework for VFL, under which three algorithms, i. e. AsySQN-SGD, -SVRG and -SAGA, are proposed.

Privacy Preserving Vertical Federated Learning

Similarity Kernel and Clustering via Random Projection Forests

no code implementations • 28 Aug 2019 • Donghui Yan, Songxiang Gu, Ying Xu, Zhiwei Qin

Similarity plays a fundamental role in many areas, including data mining, machine learning, statistics and various applied domains.

Clustering Clustering Ensemble

Cost-sensitive Selection of Variables by Ensemble of Model Sequences

no code implementations • 2 Jan 2019 • Donghui Yan, Zhiwei Qin, Songxiang Gu, Haiping Xu, Ming Shao

Many applications require the collection of data on different variables or measurements over many system performance metrics.