# Secure and Privacy-Preserving Vertical Federated Learning

Source: [Secure and Privacy-Preserving Vertical Federated Learning](<https://www.sota2.com/research/paper/69e06d217d0172140ec7cb35>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-17T05:00:04.441Z

Authors: Shan Jin, Sai Rahul Rachuri, Yizhen Wang, Anderson C.A. Nascimento, Yiwei Cai

Published: 2026-04-15T04:55:40.000Z

## Abstract

We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties. We do so by distributing the role of the aggregator in FL into multiple servers and having them run secure multiparty computation (MPC) protocols to perform model and feature aggregation and apply differential privacy (DP) to the final released model. While a naive solution would have the clients delegating the entirety of training to run in MPC between the servers, our optimized solution, which supports purely global and also global-local models updates with privacy-preserving, drastically reduces the amount of computation and communication performed using multiparty computation. The experimental results also show the effectiveness of our protocols.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Image Classification on CIFAR-10 (test) (Baseline Accuracy)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-baseline-accuracy.md>): Image Classification; CIFAR-10 (test); Test Accuracy: 74.78.

- [Image Classification on EMNIST (test)](<https://www.sota2.com/research/sota/image-classification-on-emnist-test.md>): Image Classification; EMNIST (test); Accuracy: 84.26.

- [Image Classification on CIFAR-10 (test) (Accuracy and Sparsity)](<https://www.sota2.com/research/sota/image-classification-on-cifar-10-test-accuracy-and-sparsity.md>): Image Classification; CIFAR-10 (test); Accuracy: 83.02.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.13474>)

[PDF](<https://arxiv.org/pdf/2604.13474.pdf>)