Bayesian models in Federated Learning architectures

Romhányi, Zsolt András and Kummer, Alex (2026) Bayesian models in Federated Learning architectures. Machine Learning with Applications, 25. No. 100949. ISSN 26668270

Preview

Text 1-s2.0-S2666827026001143-main (1).pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (3MB) | Preview

Abstract

This work investigates the use of Naive Bayes models in Federated Learning settings and proposes an FLNB procedure for decentralized structured classification tasks. The motivation comes from application domains such as healthcare, finance and industrial analytics, where models trained from data originating from multiple institutions would be useful, but the centralization of raw data is constrained by legal, ethical or organizational limitations. The proposed procedure combines federated preprocessing with Naive Bayes training. Continuous variables are handled by iterative federated binning, while discrete and categorical values are encoded by deterministic hashing, which provides cross-client representational consistency without requiring a shared plaintext category dictionary. For model aggregation, we introduce the DirSign method, which updates global class-conditional distributions based on correction directions rather than by directly aggregating full local count tables. The method was evaluated on four public structured classification datasets under IID and Dirichlet-based non-IID client partitions. The results show that FLNB-DirSign approaches the performance of the centralized reference model on several datasets while using less directly interpretable local distributional information. The experiments highlight the importance of discretization, smoothing, hash-space size, learning rate and client heterogeneity.

Actions (login required)

View Item