Secure and Privacy-Preserving Vertical Federated Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910130257264640 |
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| author | Jin, Shan Rachuri, Sai Rahul Wang, Yizhen Nascimento, Anderson C. A. Cai, Yiwei |
| author_facet | Jin, Shan Rachuri, Sai Rahul Wang, Yizhen Nascimento, Anderson C. A. Cai, Yiwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13474 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Secure and Privacy-Preserving Vertical Federated Learning Jin, Shan Rachuri, Sai Rahul Wang, Yizhen Nascimento, Anderson C. A. Cai, Yiwei Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing 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. |
| title | Secure and Privacy-Preserving Vertical Federated Learning |
| topic | Cryptography and Security Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.13474 |