The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2303. 04345v2 Announce Type: replace Abstract: Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients.
FedPS is a federated preprocessing framework that uses aggregated statistics to address missing values, inconsistent formats, and heterogeneous feature scales in structured data. It employs data-sketching techniques to summarize local datasets efficiently, enabling federated algorithms for feature scaling, encoding, discretization, and missing-value imputation. The framework also extends preprocessing-related models, such as Bayesian Linear Regression, to both horizontal and vertical federated learning settings, offering communication‑efficient and consistent pipelines for practical deployments.
arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.
arXiv:2608. 09074v1 Announce Type: cross Abstract: We develop a new approach to Personalized Federated Learning across heterogeneous clients using Nonparametric Empirical Bayes (NPEB).
arXiv:2606. 31742v1 Announce Type: cross Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users.
Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.