Proof-of-Deep-Learning Consensus in Blockchain Systems

Bitcoin, as one of the most successful blockchain applications, attracts unprecedented attention and investment from both academia and industry. To maintain the consistency of transaction data, the proof-of-work consensus mechanism utilizes the brute-force algorithm for validation and implicitly hosts a competition of hardware and energy. To mine cryptocurrency more efficiently, people developed GPU mining, FPGA mining, and ASIC machine mining. However, these methods still suffer from the issue of wasting energy because the hashing algorithm has remained unchanged. Alternatively, the proof-of-useful-work consensus has been proposed to execute relatively more useful tasks to maintain consistency, therefore the ``wasted energy'' could contribute to useful work.

In the proof-of-deep-learning consensus, for the first time, I proposed the two-phase design which utilized the deep learning training process as the workload of blockchain. To further improve the consensus, I enhanced the feasibility to exploit the computation power of blockchain for deep learning algorithms. The miners can switch between different training tasks in the memory pool and align the target task.

However at this stage, all miners are still required to work on the same task in the same block, as a result, the majority of energy has been wasted due to the computation redundancy. Therefore, I introduced the collaboration strategy to reduce redundancy.

In the proof-of-federated-learning-subchain work, I categorized different miner roles and emphasized the importance of data contributors. Under this framework, miners can select partners based on average response time and training data value to form pools under a federated learning framework.

In the series of my work, I aim to recycle the "waste" resources to train deep learning models which powered many applications behind the scenes in our daily lives. Beginning with the initial proof-of-deep-learning consensus, I continued to improve feasibility, reduce redundancy, and initialize the framework of the next-generation consensus which is optimized for the deep learning training tasks as the workload of blockchain.