SPDL: Blockchain-secured and Privacy-preserving Decentralized Learning

TL;DR

SPDL is a novel decentralized learning scheme that combines blockchain, Byzantine fault tolerance, and differential privacy to ensure secure, private, and efficient machine learning across distributed devices.

Contribution

It introduces a comprehensive system integrating blockchain, BFT consensus, gradient aggregation, and differential privacy for secure and private decentralized learning.

Findings

SPDL achieves strong security and privacy guarantees.

The scheme demonstrates efficient convergence in experiments.

It maintains robustness against Byzantine faults.

Abstract

Decentralized learning involves training machine learning models over remote mobile devices, edge servers, or cloud servers while keeping data localized. Even though many studies have shown the feasibility of preserving privacy, enhancing training performance or introducing Byzantine resilience, but none of them simultaneously considers all of them. Therefore we face the following problem: \textit{how can we efficiently coordinate the decentralized learning process while simultaneously maintaining learning security and data privacy?} To address this issue, in this paper we propose SPDL, a blockchain-secured and privacy-preserving decentralized learning scheme. SPDL integrates blockchain, Byzantine Fault-Tolerant (BFT) consensus, BFT Gradients Aggregation Rule (GAR), and differential privacy seamlessly into one system, ensuring efficient machine learning while maintaining data privacy, Byzantine fault tolerance, transparency, and traceability. To validate our scheme, we provide rigorous analysis on convergence and regret in the presence of Byzantine nodes. We also build a SPDL prototype and conduct extensive experiments to demonstrate that SPDL is effective and efficient with strong security and privacy guarantees.