mlsanityData to 14 Oct 2026

Secure and Privacy-Preserving Vertical Federated Learning

arXiv 2604.13474 ↗First posted 15 Apr 2026cs.CR cs.AI cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 85 references that are in the map. First posted on arXiv 15 Apr 2026.

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31 of 85 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,417
  5. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  6. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  7. 2021Opacus: User-Friendly Differential Privacy Library in PyTorchcited by 383
  8. 2021Enhanced Membership Inference Attacks against Machine Learning Modelscited by 248
  9. 2018Subsampled R'enyi Differential Privacy and Analytical Moments Accountantcited by 236
  10. 2021The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregationcited by 128
  11. 2021CAFE: Catastrophic Data Leakage in Vertical Federated Learningcited by 76
  12. 2020NoPeek: Information leakage reduction to share activations in distributed deep learningcited by 66
19 more
  1. 2022Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streamscited by 59
  2. 2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
  3. 2022Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learningcited by 48
  4. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  5. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  6. 2021Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errorscited by 25
  7. 2020Hybrid Differentially Private Federated Learning on Vertically Partitioned Datacited by 22
  8. 2023Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learningcited by 17
  9. 2021Secure Quantized Training for Deep Learningcited by 15
  10. 2024Banded Square Root Matrix Factorization for Differentially Private Model Trainingcited by 12
  11. 2022Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMMcited by 10
  12. 2024Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learningcited by 9
  13. 2021Tetrad: Actively Secure 4PC for Secure Training and Inferencecited by 8
  14. 2024Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Datacited by 8
  15. 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Ownerscited by 5
  16. 2022EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Partycited by 5
  17. 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
  18. 2025OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learningcited by 1
  19. 2025Covert Attacks on Machine Learning Training in Passively Secure MPCcited by 1

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