SEDULity: A Proof-of-Learning Framework for Distributed and Secure Blockchains with Efficient Useful Work

TL;DR

SEDULity introduces a secure, efficient, and fully distributed Proof-of-Learning framework that leverages machine learning training tasks as useful work, enhancing blockchain sustainability without compromising security.

Contribution

It proposes a novel PoL framework that encodes blockchain templates into ML training, ensuring security, decentralization, and efficiency, with a theoretical incentive mechanism and validated simulation results.

Findings

Framework trains ML models efficiently and securely

Incentive mechanism encourages honest participation

Simulation confirms system performance and security

Abstract

The security and decentralization of Proof-of-Work (PoW) have been well-tested in existing blockchain systems. However, its tremendous energy waste has raised concerns about sustainability. Proof-of-Useful-Work (PoUW) aims to redirect the meaningless computation to meaningful tasks such as solving machine learning (ML) problems, giving rise to the branch of Proof-of-Learning (PoL). While previous studies have proposed various PoLs, they all, to some degree, suffer from security, decentralization, or efficiency issues. In this paper, we propose a PoL framework that trains ML models efficiently while maintaining blockchain security in a fully distributed manner. We name the framework SEDULity, which stands for a Secure, Efficient, Distributed, and Useful Learning-based blockchain system. Specifically, we encode the template block into the training process and design a useful function that is difficult to solve but relatively easy to verify, as a substitute for the PoW puzzle. We show that our framework is distributed, secure, and efficiently trains ML models. We further demonstrate that the proposed PoL framework can be extended to other types of useful work and design an incentive mechanism to incentivize task verification. We show theoretically that a rational miner is incentivized to train fully honestly with well-designed system parameters. Finally, we present simulation results to demonstrate the performance of our framework and validate our analysis.