# FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data

arXiv: https://arxiv.org/abs/2103.03918 · HTML: https://mlsanity.com/paper/2103.03918/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption](https://mlsanity.com/paper/1711.10677/) (2017)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://mlsanity.com/paper/1812.00910/) (2018)
- [Detailed comparison of communication efficiency of split learning and federated learning](https://mlsanity.com/paper/1909.09145/) (2019)
- [Variational Federated Multi-Task Learning](https://mlsanity.com/paper/1906.06268/) (2019)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1912.05897/) (2019)
- [A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression](https://mlsanity.com/paper/1912.00513/) (2019)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2008.06197/) (2020)
- [Entity Resolution and Federated Learning get a Federated Resolution](https://mlsanity.com/paper/1803.04035/) (2018)
- [Hybrid Differentially Private Federated Learning on Vertically Partitioned Data](https://mlsanity.com/paper/2009.02763/) (2020)

## Built on by (most cited first, 30 of 31)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges](https://mlsanity.com/paper/2211.07893/) (2022)
- [Vertical Federated Learning: A Structured Literature Review](https://mlsanity.com/paper/2212.00622/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Continual Horizontal Federated Learning for Heterogeneous Data](https://mlsanity.com/paper/2203.02108/) (2022)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [Responsible Artificial Intelligence: A Structured Literature Review](https://mlsanity.com/paper/2403.06910/) (2024)
- [Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning](https://mlsanity.com/paper/2404.10110/) (2024)
- [TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2501.05053/) (2025)
- [GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data](https://mlsanity.com/paper/2303.09531/) (2023)
- [Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection](https://mlsanity.com/paper/2401.10765/) (2024)
- [A Differentially Private Blockchain-Based Approach for Vertical Federated Learning](https://mlsanity.com/paper/2407.07054/) (2024)
- [The Sherpa.ai Blind Vertical Federated Learning Paradigm to Minimize the Number of Communications](https://mlsanity.com/paper/2510.17901/) (2025)
- [Quadratic Functional Encryption for Secure Training in Vertical Federated Learning](https://mlsanity.com/paper/2305.08358/) (2023)
- [Federated Learning over Harmonized Data Silos](https://mlsanity.com/paper/2305.08985/) (2023)
- [Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings](https://mlsanity.com/paper/2308.02362/) (2023)
- [Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection](https://mlsanity.com/paper/2310.19304/) (2023)
- [A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT](https://mlsanity.com/paper/2410.01644/) (2024)
- [Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization](https://mlsanity.com/paper/2502.20565/) (2025)
- [OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning](https://mlsanity.com/paper/2504.15995/) (2025)
- [Position: Life-Logging Video Streams Make the Privacy-Utility Trade-off Inevitable](https://mlsanity.com/paper/2605.10404/) (2026)
- [Efficient and Privacy Preserving Group Signature for Federated Learning](https://mlsanity.com/paper/2207.05297/) (2022)
- [EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning](https://mlsanity.com/paper/2310.13367/) (2023)
- [FedDMF: Privacy-Preserving User Attribute Prediction using Deep Matrix Factorization](https://mlsanity.com/paper/2312.15420/) (2023)
- [Survey of Privacy Threats and Countermeasures in Federated Learning](https://mlsanity.com/paper/2402.00342/) (2024)
- [PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy](https://mlsanity.com/paper/2501.13916/) (2025)
- [FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning](https://mlsanity.com/paper/2503.09643/) (2025)
- [On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attack](https://mlsanity.com/paper/2507.02376/) (2025)
- [Differentially Private Decentralized Dataset Synthesis Through Randomized Mixing with Correlated Noise](https://mlsanity.com/paper/2509.10385/) (2025)

## Abstract (arXiv)

Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this paradigm, only model updates, such as model weights or gradients, are shared. Many existing approaches have focused on horizontal FL, where each party has the entire feature set and labels in the training data set. However, many real scenarios follow a vertically-partitioned FL setup, where a complete feature set is formed only when all the datasets from the parties are combined, and the labels are only available to a single party. Privacy-preserving vertical FL is challenging because complete sets of labels and features are not owned by one entity. Existing approaches for vertical FL require multiple peer-to-peer communications among parties, leading to lengthy training times, and are restricted to (approximated) linear models and just two parties. To close this gap, we propose FedV, a framework for secure gradient computation in vertical settings for several widely used ML models such as linear models, logistic regression, and support vector machines. FedV removes the need for peer-to-peer communication among parties by using functional encryption schemes; this allows FedV to achieve faster training times. It also works for larger and changing sets of parties. We empirically demonstrate the applicability for multiple types of ML models and show a reduction of 10%-70% of training time and 80% to 90% in data transfer with respect to the state-of-the-art approaches.