Secure and Privacy-Preserving Vertical Federated Learning

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Cloud Computing & IT Infrastructure · Depth: Expert, quick

Summary

A novel end-to-end privacy-preserving framework for vertical federated learning (FL) is proposed, addressing scenarios where features are split across clients and labels are not universally shared. Submitted on April 15, 2026, the framework introduces three efficient protocols tailored for different deployment scenarios, ensuring both input and output privacy. It achieves this by distributing the aggregator's role among multiple servers, which then execute secure multiparty computation (MPC) protocols for model and feature aggregation. Additionally, differential privacy (DP) is applied to the final released model. The optimized solution supports both purely global and global-local model updates, significantly reducing MPC-related computation and communication compared to a naive approach where clients delegate all training to MPC between servers. Experimental results validate the protocols' effectiveness.

Key takeaway

For research scientists developing secure machine learning systems, this framework offers a robust approach to vertical federated learning. You should consider integrating distributed aggregation with secure multiparty computation and differential privacy to protect both input and output data effectively, especially in scenarios with split features and non-shared labels. This can drastically reduce computational overhead compared to full MPC delegation.

Key insights

A new framework enhances vertical federated learning privacy via distributed aggregation, MPC, and differential privacy.

Principles

• Distribute aggregator roles for enhanced security.
• Combine MPC and DP for comprehensive privacy.

Method

The method distributes the FL aggregator role across multiple servers, which perform model and feature aggregation using secure multiparty computation (MPC) protocols, followed by differential privacy (DP) application to the final model.

In practice

• Implement distributed aggregation for FL.
• Apply DP to final model releases.

Topics

• Vertical Federated Learning
• Privacy-Preserving AI
• Secure Multiparty Computation
• Differential Privacy
• Distributed Aggregation

Best for: Research Scientist, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.