A Bayesian Vertical Federated Learning Framework for Multivariate Reduced-Rank High-Dimensional Regression

arXiv:2609.22654 · stat.ML, cs.LG, stat.CO · Submitted 2026-09-18 · Read on arXiv

stat.ML, cs.LG, stat.CO

Submitted: 2026-09-18

Updated: 2026-09-26

Code: https://github.com/brighalvy/BayesVFLReg

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Federated Learning — 29× in this paper · explained in 14 episodes
• Sparsity — 20× in this paper · explained in 4 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Differential Privacy — 4× in this paper · explained in 4 episodes
• Uncertainty Quantification — 4× in this paper · explained in 8 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Index Code Region of Interest — 2× in this paper
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Mean Squared Errors — 1× in this paper
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Non-asymptotic Analysis — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Statistical properties of sketching algorithms
• Asymptotics for Sketching in Least Squares Regression
• Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
• A Federated Learning Framework for Smart Grids: Securing Power Traces in Collaborative Learning
• Introduction to the non-asymptotic analysis of random matrices
• Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey