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

arXiv: https://arxiv.org/abs/2512.13666 · HTML: https://mlsanity.com/paper/2512.13666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 15 Dec 2025.

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

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Proof of Federated Learning: A Novel Energy-recycling Consensus Algorithm](https://mlsanity.com/paper/1912.11745/) (2019)
- ["Adversarial Examples" for Proof-of-Learning](https://mlsanity.com/paper/2108.09454/) (2021)
- [Proof-of-Learning is Currently More Broken Than You Think](https://mlsanity.com/paper/2208.03567/) (2022)
- [A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks](https://mlsanity.com/paper/1805.02707/) (2018)
- [Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning](https://mlsanity.com/paper/1903.09800/) (2019)
- [Secure and Efficient Federated Learning Through Layering and Sharding Blockchain](https://mlsanity.com/paper/2104.13130/) (2021)
- [Energy-recycling Blockchain with Proof-of-Deep-Learning](https://mlsanity.com/paper/1902.03912/) (2019)
- [A Platform-Free Proof of Federated Learning Consensus Mechanism for Sustainable Blockchains](https://mlsanity.com/paper/2208.12046/) (2022)
- [A Proof of Useful Work for Artificial Intelligence on the Blockchain](https://mlsanity.com/paper/2001.09244/) (2020)
- [Proof-of-Learning with Incentive Security](https://mlsanity.com/paper/2404.09005/) (2024)
- [Coinami: A Cryptocurrency with DNA Sequence Alignment as Proof-of-work](https://mlsanity.com/paper/1602.03031/) (2016)
- [Proof-of-Search: Combining Blockchain Consensus Formation with Solving Optimization Problems](https://mlsanity.com/paper/1908.01915/) (2019)
- [Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning](https://mlsanity.com/paper/2307.16342/) (2023)
- [Optimization-based Proof of Useful Work: Framework, Modeling, and Security Analysis](https://mlsanity.com/paper/2405.19027/) (2024)
- [BagChain: A Dual-functional Blockchain Leveraging Bagging-based Distributed Learning](https://mlsanity.com/paper/2502.11464/) (2025)
- [Proofs of Useful Work from Arbitrary Matrix Multiplication](https://mlsanity.com/paper/2504.09971/) (2025)

## Abstract (arXiv)

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.