26 citations · 29 across the 5 of their papers we have counts for
4 papers · 1 filter
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Zhaomin Wu, Junyi Hou, Yiqun Diao +1
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learn…
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…