A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
<|reference_start|>A Differentially Private Blockchain-Based Approach for Vertical Federated Learning: 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.<|reference_end|>
@article{tran2024a,
    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},
    archivePrefix={arXiv},
    eprint={2407.07054},
    primaryClass={cs.CR cs.ET cs.LG}
}