# Proof of Deep Learning: Approaches, Challenges, and Future Directions

arXiv: https://arxiv.org/abs/2308.16730 · HTML: https://mlsanity.com/paper/2308.16730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 60 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Compressing Deep Convolutional Networks using Vector Quantization](https://mlsanity.com/paper/1412.6115/) (2014)
- [Proof-of-Learning: Definitions and Practice](https://mlsanity.com/paper/2103.05633/) (2021)
- [Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-based Distributed Deep Learning](https://mlsanity.com/paper/1903.09800/) (2019)
- [Energy-recycling Blockchain with Proof-of-Deep-Learning](https://mlsanity.com/paper/1902.03912/) (2019)
- [DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services](https://mlsanity.com/paper/1904.07349/) (2019)
- [Challenges of Proof-of-Useful-Work (PoUW)](https://mlsanity.com/paper/2209.03865/) (2022)

## Abstract (arXiv)

The rise of computational power has led to unprecedented performance gains for deep learning models. As more data becomes available and model architectures become more complex, the need for more computational power increases. On the other hand, since the introduction of Bitcoin as the first cryptocurrency and the establishment of the concept of blockchain as a distributed ledger, many variants and approaches have been proposed. However, many of them have one thing in common, which is the Proof of Work (PoW) consensus mechanism. PoW is mainly used to support the process of new block generation. While PoW has proven its robustness, its main drawback is that it requires a significant amount of processing power to maintain the security and integrity of the blockchain. This is due to applying brute force to solve a hashing puzzle. To utilize the computational power available in useful and meaningful work while keeping the blockchain secure, many techniques have been proposed, one of which is known as Proof of Deep Learning (PoDL). PoDL is a consensus mechanism that uses the process of training a deep learning model as proof of work to add new blocks to the blockchain. In this paper, we survey the various approaches for PoDL. We discuss the different types of PoDL algorithms, their advantages and disadvantages, and their potential applications. We also discuss the challenges of implementing PoDL and future research directions.