تقرير
End-to-End Verifiable Decentralized Federated Learning
| العنوان: | End-to-End Verifiable Decentralized Federated Learning |
|---|---|
| المؤلفون: | Lee, Chaehyeon, Heiss, Jonathan, Tai, Stefan, Hong, James Won-Ki |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing |
| الوصف: | 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. Comment: 9 pages, 5 figures, This article has been accepted for presentation at the IEEE International Conference on Blockchain and Cryptocurrency (ICBC 2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.12623 |
| رقم الأكسشن: | edsarx.2404.12623 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |