28 citations · 43 across the 4 of their papers we have counts for
5 papers · 1 filter
Exploiting Label Skews in Federated Learning with Model Concatenation
Yiqun Diao, Qinbin Li, Bingsheng He
Federated Learning (FL) has emerged as a promising solution to perform deep learning on different data owners without exchanging raw data. However, non-IID data has been a key chal…
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Yiqun Diao, Yutong Yang, Qinbin Li +2
How to get insights from relational data streams in a timely manner is a hot research topic. Data streams can present unique challenges, such as distribution drifts, outliers, emer…
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 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…