# A Differentially Private Blockchain-Based Approach for Vertical Federated Learning

arXiv: https://arxiv.org/abs/2407.07054 · HTML: https://mlsanity.com/paper/2407.07054/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [A Hybrid Approach to Privacy-Preserving Federated Learning](https://mlsanity.com/paper/1812.03224/) (2018)
- [LDP-Fed: Federated Learning with Local Differential Privacy](https://mlsanity.com/paper/2006.03637/) (2020)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data](https://mlsanity.com/paper/2206.08330/) (2022)
- [FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data](https://mlsanity.com/paper/2103.03918/) (2021)
- [Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning](https://mlsanity.com/paper/2108.08930/) (2021)
- [HotStuff: BFT Consensus in the Lens of Blockchain](https://mlsanity.com/paper/1803.05069/) (2018)
- [A Fast Blockchain-based Federated Learning Framework with Compressed Communications](https://mlsanity.com/paper/2208.06095/) (2022)
- [DFL: High-Performance Blockchain-Based Federated Learning](https://mlsanity.com/paper/2110.15457/) (2021)

## Built on by (most cited first, 3 of 3)

- [FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphs](https://mlsanity.com/paper/2608.24846/) (2026)
- [FedV-KGQA in Practice: Design Lessons and an Interactive Prototype](https://mlsanity.com/paper/2609.13661/) (2026)
- [Cost Characterization of Vertically Partitioned Federated Knowledge Graphs](https://mlsanity.com/paper/2609.13664/) (2026)

## Abstract (arXiv)

We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applications. DP-BBVFL uses a smart contract to aggregate the feature representations, i.e., the embeddings, from clients transparently. We apply local differential privacy to provide privacy for embeddings stored on a blockchain, hence protecting the original data. We provide the first prototype application of differential privacy with blockchain for vertical federated learning. Our experiments with medical data show that DP-BBVFL achieves high accuracy with a tradeoff in training time due to on-chain aggregation. This innovative fusion of differential privacy and blockchain technology in DP-BBVFL could herald a new era of collaborative and trustworthy machine learning applications across several decentralized application domains.