# Can Blockchains Reliably Train Machine Learning Models?

arXiv: https://arxiv.org/abs/2307.07066 · HTML: https://mlsanity.com/paper/2307.07066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 78 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://mlsanity.com/paper/1901.05758/) (2019)
- [Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains](https://mlsanity.com/paper/1801.10228/) (2018)
- [Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters](https://mlsanity.com/paper/2109.01313/) (2021)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- [A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks](https://mlsanity.com/paper/1805.02707/) (2018)
- [Consensus in the Age of Blockchains](https://mlsanity.com/paper/1711.03936/) (2017)
- [Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning](https://mlsanity.com/paper/1903.09800/) (2019)
- [Anatomy of a Stablecoin's failure: the Terra-Luna case](https://mlsanity.com/paper/2207.13914/) (2022)
- [Energy-recycling Blockchain with Proof-of-Deep-Learning](https://mlsanity.com/paper/1902.03912/) (2019)
- [Decentralization in Bitcoin and Ethereum Networks](https://mlsanity.com/paper/1801.03998/) (2018)
- [A Survey of Layer-Two Blockchain Protocols](https://mlsanity.com/paper/2204.08032/) (2022)
- [Understanding the Motivations, Challenges and Needs of Blockchain Software Developers: A Survey](https://mlsanity.com/paper/1811.04169/) (2018)
- [Analyzing Voting Power in Decentralized Governance: Who controls DAOs?](https://mlsanity.com/paper/2204.01176/) (2022)
- [Understanding Blockchain Governance: Analyzing Decentralized Voting to Amend DeFi Smart Contracts](https://mlsanity.com/paper/2305.17655/) (2023)

## Built on by (most cited first, 1 of 1)

- [Web3 Meets AI Marketplace: Exploring Opportunities, Analyzing Challenges, and Suggesting Solutions](https://mlsanity.com/paper/2310.19099/) (2023)

## Abstract (arXiv)

Large proof of work (PoW) networks allow anyone to earn rewards by running computation-intensive hash puzzles for profit, yet they typically consume electricity comparable to that of medium-sized countries. Repurposing computing resources from hash puzzles to machine learning training can benefit the energy sector as a whole, since this computing power is no longer wasted on solving hash puzzles but is instead used to train machine learning models that provide value across different application domains. However, major technical gaps currently prevent this integration. To bridge these gaps, we introduce proof of training (PoT), a protocol that directs mining power toward verifiable training of machine learning models while preserving PoW's incentives for participation and growth. We study PoT by theoretically identifying the blockchain structure that best meets the goals of training reliability, security, and scalability, and we further evaluate it by implementing a decentralized training network. Our results indicate considerable potential, including high task throughput, strong robustness, and improved network security.