1.4k citations · 2k across the 34 of their papers we have counts for
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A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning
Zhaomin Wu, Qinbin Li, Bingsheng He
Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where pa…
Model-Contrastive Federated Learning
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to ha…
Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload
Johan Kok Zhi Kang, Gaurav, Sien Yi Tan +3
The use of deep learning models for forecasting the resource consumption patterns of SQL queries have recently been a popular area of study. With many companies using cloud platfor…
Federated Learning on Non-IID Data Silos: An Experimental Study
Qinbin Li, Yiqun Diao, Quan Chen +1
Due to the increasing privacy concerns and data regulations, training data have been increasingly fragmented, forming distributed databases of multiple "data silos" (e.g., within d…
Practical One-Shot Federated Learning for Cross-Silo Setting
Qinbin Li, Bingsheng He, Dawn Song
Federated learning enables multiple parties to collaboratively learn a model without exchanging their data. While most existing federated learning algorithms need many rounds to co…
The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systems
Sixu Hu, Yuan Li, Xu Liu +3
This paper presents and characterizes an Open Application Repository for Federated Learning (OARF), a benchmark suite for federated machine learning systems. Previously available b…
Practical Federated Gradient Boosting Decision Trees
Qinbin Li, Zeyi Wen, Bingsheng He
Gradient Boosting Decision Trees (GBDTs) have become very successful in recent years, with many awards in machine learning and data mining competitions. There have been several rec…
Adaptive Kernel Value Caching for SVM Training
Qinbin Li, Zeyi Wen, Bingsheng He
Support Vector Machines (SVMs) can solve structured multi-output learning problems such as multi-label classification, multiclass classification and vector regression. SVM training…
Privacy-Preserving Gradient Boosting Decision Trees
Qinbin Li, Zhaomin Wu, Zeyi Wen +1
The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model for various tasks in recent years. In this paper, we study how to improve model accuracy of GBDT whil…
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
Qinbin Li, Zeyi Wen, Zhaomin Wu +5
Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As…