Communication Dans Un Congrès Année : 2025

VerifBFL: Leveraging zk-SNARKs for a Verifiable Blockchained Federated Learning

Résumé

Blockchain-based Federated Learning (BFL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and in-crementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training accuracy and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.

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hal-05294910 , version 1 (02-10-2025)

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Ahmed Ayoub Bellachia, Mouhamed Amine Bouchiha, Yacine Ghamri-Doudane, Mourad Rabah. VerifBFL: Leveraging zk-SNARKs for a Verifiable Blockchained Federated Learning. NOMS 2025-2025 IEEE Network Operations and Management Symposium, May 2025, Honolulu, United States. pp.01-09, ⟨10.1109/NOMS57970.2025.11073628⟩. ⟨hal-05294910⟩

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