Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
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Abstract (arXiv)
Bitcoin's Proof of Work (PoW) mechanism, while central to achieving decentralized consensus, has long been criticized for excessive energy use and hardware inefficiencies citedevries2018bitcoin, 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 citeli2017securing. 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 citenakamoto2008bitcoin. By integrating digital signatures and SHA-256 hashing citenist2015sha, 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.
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References · 5
- 2020Scaling Laws for Neural Language Modelscited by 6,586
- 2015Experiments on Parallel Training of Deep Neural Network using Model Averagingcited by 14
- 2013GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Trainingcited by 7
- 2024Beyond Gradient Averaging in Parallel Optimization: Improved Robustness through Gradient Agreement Filteringcited by 3
- 2018Stochastic Gradient Descent on Highly-Parallel Architecturescited by 1
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