Faculty, Staff and Student Publications
Language
English
Publication Date
5-1-2025
Journal
IEEE Transactions on Knowledge and Data Engineering
DOI
10.1109/TKDE.2025.3537403
PMID
40322292
PMCID
PMC12048026
PubMedCentral® Posted Date
5-1-2026
PubMedCentral® Full Text Version
Author MSS
Abstract
Federated learning (FL), a decentralized machine learning approach, offers great performance while alleviating autonomy and confidentiality concerns. Despite FL's popularity, how to deal with missing values in a federated manner is not well understood. In this work, we initiate a study of federated imputation of missing values, particularly in complex scenarios, where missing data heterogeneity exists and the state-of-the-art (SOTA) approaches for federated imputation suffer from significant loss in imputation quality. We propose Cafe, a personalized FL approach for missing data imputation. Cafe is inspired from the observation that heterogeneity can induce differences in observable and missing data distribution across clients, and that these differences can be leveraged to improve the imputation quality. Cafe computes personalized weights that are automatically calibrated for the level of heterogeneity, which can remain unknown, to develop personalized imputation models for each client. An extensive empirical evaluation over a variety of settings demonstrates that Cafe matches the performance of SOTA baselines in homogeneous settings while significantly outperforming the baselines in heterogeneous settings.
Keywords
Federated Learning, Missing Data Imputation, Data Quality, Data Heterogeneity
Published Open-Access
yes
Recommended Citation
Min, Sitao; Asif, Hafiz; Wang, Xinyue; et al., "Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity" (2025). Faculty, Staff and Student Publications. 931.
https://digitalcommons.library.tmc.edu/uthshis_docs/931