3 papers
cs.CR2026
Exploring CKKS Parameter Trade-offs for Privacy-Preserving Personalized Federated Learning
Kamolchanok Saengtong, Phanwadee Sinthong, Norrathep Rattanavipanon
Privacy-preserving Personalized Federated Learning (PFL) enables clients to collaboratively train personalized models without exposing raw data, but exchanged model updates remain…
cs.DB2020
PolyFrame: A Retargetable Query-based Approach to Scaling DataFrames (Extended Version)
Phanwadee Sinthong, Michael J. Carey
In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision m…
cs.DB2019
AFrame: Extending DataFrames for Large-Scale Modern Data Analysis (Extended Version)
Phanwadee Sinthong, Michael J. Carey
Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distrib…