Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation

TL;DR

This paper proposes replacing energy-intensive Proof of Work in blockchain with a collaborative machine learning training process, using contribution-based rewards and digital signatures to maintain security and incentivize productive work.

Contribution

It introduces a hybrid blockchain architecture that substitutes PoW with a cloud-based collaborative training framework, enhancing sustainability and computational utility.

Findings

Reduces energy consumption compared to traditional PoW

Ensures security through digital signatures and hashing

Aligns incentives with meaningful computational work

Abstract

Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies \cite{devries2018bitcoin, truby2018decarbonizing}. This paper introduces a hybrid architecture that replaces Bitcoin's traditional PoW with a centralized, cloud-based collaborative training framework. In this model, miners contribute computing resources to train segments of horizontally scaled machine learning models on preprocessed datasets, ensuring privacy and generating meaningful outputs \cite{li2017securing}. A central server evaluates contributions using two metrics: number of parameters trained and reduction in model loss during each cycle. At the end of every cycle, a weighted lottery selects the winning miner, who receives a digitally signed certificate. This certificate serves as a verifiable substitute for PoW and grants the right to append a block to the blockchain \cite{nakamoto2008bitcoin}. By integrating digital signatures and SHA-256 hashing \cite{nist2015sha}, the system preserves blockchain integrity while redirecting energy toward productive computation. The proposed approach addresses the sustainability concerns of traditional mining by converting resource expenditure into socially valuable work, aligning security incentives with real-world computational progress.