Multi-task learning for PBFT optimisation in permissioned blockchains

Abstract

Finance, supply chains, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT), which tolerates up to one-third of Byzantine nodes, performs within partially synchronous systems, and has superior throughput compared to other protocols. It has, however, an important bandwidth consumption: $2N(N-1)$ messages are exchanged in a system composed of N nodes to validate only one block.

It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security, rapidity, and availability. In this paper, we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus. It reflects their level of security, rapidity, and availability throughout the consensus. We first investigate different Single-Task Learning (STL) techniques to classify the nodes within our dataset. Then, using Multi-Task Learning (MTL) techniques, the results are much more interesting, with classification accuracies over 98%. Integrating node classification as a preliminary step to the PBFT protocol optimizes the consensus. In the best cases, it is able to reduce the latency by up to 94% and the communication traffic by up to 99%.