17 citations · 20 across the 4 of their papers we have counts for
4 papers
Guided Model Merging for Hybrid Data Learning: Leveraging Centralized Data to Refine Decentralized Models
Junyi Zhu, Ruicong Yao, Taha Ceritli +6
Current network training paradigms primarily focus on either centralized or decentralized data regimes. However, in practice, data availability often exhibits a hybrid nature, wher…
A Powerful Random Forest Featuring Linear Extensions (RaFFLE)
Jakob Raymaekers, Peter J. Rousseeuw, Thomas Servotte +2
Random forests are widely used in regression. However, the decision trees used as base learners are poor approximators of linear relationships. To address this limitation we propos…
Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning
Junyi Zhu, Ruicong Yao, Matthew B. Blaschko
In Federated Learning (FL) and many other distributed training frameworks, collaborators can hold their private data locally and only share the network weights trained with the loc…
Fast Linear Model Trees by PILOT
Jakob Raymaekers, Peter J. Rousseeuw, Tim Verdonck +1
Linear model trees are regression trees that incorporate linear models in the leaf nodes. This preserves the intuitive interpretation of decision trees and at the same time enables…