A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
This addresses privacy concerns for decentralized applications in domains like healthcare, though it appears incremental as it fuses existing techniques.
The paper tackles the problem of ensuring privacy and verifiability in vertical federated learning by proposing DP-BBVFL, which combines differential privacy with blockchain technology; experiments on medical data show it achieves high accuracy but increases training time due to on-chain aggregation.
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.