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

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. 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, which is 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. This is due to applying brute force to solve a hashing puzzle. To utilize the computational power available for 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 a 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. To the best of our knowledge, this is the first comprehensive study that explores and systematically surveys this topic, highlighting its key challenges, advancements, and open research directions.