Substituting Proof of Work in Blockchain with Training-Verified Collaborative Model Computation
This addresses sustainability concerns in blockchain for users and developers by converting mining energy into socially valuable work, though it is incremental as it modifies an existing mechanism rather than introducing a new paradigm.
This paper tackles the energy inefficiency of Bitcoin's Proof of Work by replacing it with a centralized collaborative training framework where miners train machine learning models, using metrics like parameter count and loss reduction to select winners via a weighted lottery for block appending, resulting in a system that redirects energy toward productive computation.
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.