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.
@article{arxiv.2407.07054,
title = {A Differentially Private Blockchain-Based Approach for Vertical Federated Learning},
author = {Linh Tran and Sanjay Chari and Md. Saikat Islam Khan and Aaron Zachariah and Stacy Patterson and Oshani Seneviratne},
journal= {arXiv preprint arXiv:2407.07054},
year = {2024}
}