Search Results for author: Zhaomin Wu

Found 6 papers, 6 papers with code

VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks

1 code implementation5 Jul 2023 Zhaomin Wu, Junyi Hou, Bingsheng He

However, due to privacy restrictions, few public real-world VFL datasets exist for algorithm evaluation, and these represent a limited array of feature distributions.

Feature Correlation Feature Importance +1

Practical Vertical Federated Learning with Unsupervised Representation Learning

1 code implementation13 Aug 2022 Zhaomin Wu, Qinbin Li, Bingsheng He

As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications.

Privacy Preserving Representation Learning +1

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems

1 code implementation14 Jun 2020 Sixu Hu, Yuan Li, Xu Liu, Qinbin Li, Zhaomin Wu, Bingsheng He

This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems.

Federated Learning

Privacy-Preserving Gradient Boosting Decision Trees

2 code implementations11 Nov 2019 Qinbin Li, Zhaomin Wu, Zeyi Wen, Bingsheng He

Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds.

Privacy Preserving

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection

1 code implementation23 Jul 2019 Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He

By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.

BIG-bench Machine Learning Federated Learning +1

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