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

TL;DR

VerifBFL introduces a blockchain-based federated learning framework that uses zk-SNARKs and differential privacy to ensure verifiability, security, and privacy without relying on trust assumptions.

Contribution

It presents a novel trustless, verifiable federated learning framework combining blockchain, cryptographic proofs, and differential privacy for enhanced security and transparency.

Findings

Proof generation for training takes less than 81 seconds.

Proof generation for aggregation takes less than 2 seconds.

On-chain verification takes less than 0.6 seconds.

Abstract

Blockchain-based Federated Learning (FL) 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 incrementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training 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.