mlsanityData to 7 Oct 2026

A Differentially Private Blockchain-Based Approach for Vertical Federated Learning

arXiv 2407.07054 ↗First posted 9 Jul 2024cs.CR cs.ET cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

13 of 35 references.

References · 13

  1. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  3. 2014Understanding Deep Image Representations by Inverting Themcited by 399
  4. 2018A Hybrid Approach to Privacy-Preserving Federated Learningcited by 208
  5. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 129
  6. 2021Deep Learning with Label Differential Privacycited by 113
  7. 2021Label Leakage and Protection in Two-party Split Learningcited by 87
  8. 2022Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Datacited by 36
  9. 2021FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Datacited by 31
  10. 2021Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioningcited by 6
  11. 2018HotStuff: BFT Consensus in the Lens of Blockchaincited by 5
  12. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
1 more
  1. 2021DFL: High-Performance Blockchain-Based Federated Learningcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026FedV-KGQA: Multi-Hop Question Answering over Vertically Partitioned Knowledge Graphscited by 2
  2. 2026FedV-KGQA in Practice: Design Lessons and an Interactive Prototypecited by 0
  3. 2026Cost Characterization of Vertically Partitioned Federated Knowledge Graphscited by 0