mlsanityData to 7 Oct 2026

End-to-End Verifiable Decentralized Federated Learning

arXiv 2404.12623 ↗First posted 19 Apr 2024cs.LG cs.CR cs.DCcited by 0

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Abstract (arXiv)

Verifiable decentralized federated learning (FL) systems combining blockchains and zero-knowledge proofs (ZKP) make the computational integrity of local learning and global aggregation verifiable across workers. However, they are not end-to-end: data can still be corrupted prior to the learning. In this paper, we propose a verifiable decentralized FL system for end-to-end integrity and authenticity of data and computation extending verifiability to the data source. Addressing an inherent conflict of confidentiality and transparency, we introduce a two-step proving and verification (2PV) method that we apply to central system procedures: a registration workflow that enables non-disclosing verification of device certificates and a learning workflow that extends existing blockchain and ZKP-based FL systems through non-disclosing data authenticity proofs. Our evaluation on a prototypical implementation demonstrates the technical feasibility with only marginal overheads to state-of-the-art solutions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 43 references that are in the map. First posted on arXiv 19 Apr 2024.

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References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  4. 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
  5. 2021Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcarecited by 15
  6. 2022Towards Verifiable Federated Learningcited by 11
  7. 2023Zero-Knowledge Proof-based Practical Federated Learning on Blockchaincited by 9
  8. 2022Advancing Blockchain-based Federated Learning through Verifiable Off-chain Computationscited by 7
  9. 2019Towards Fair and Privacy-Preserving Federated Deep Modelscited by 5
  10. 2021Blockchain-based Trustworthy Federated Learning Architecturecited by 5
  11. 2022Non-Disclosing Credential On-chaining for Blockchain-based Decentralized Applicationscited by 2
  12. 2021Trustworthy Pre-Processing of Sensor Data in Data On-chaining Workflows for Blockchain-based IoT Applicationscited by 1

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