Contrastive Learning for Efficient Transaction Validation in UTXO-based Blockchains

TL;DR

This paper presents a contrastive learning-based method to optimize UTXO sharding and transaction routing in blockchains, reducing communication overhead and improving scalability by embedding transaction relationships.

Contribution

It introduces a novel ML framework combining contrastive and unsupervised learning to embed UTXO relationships, enabling efficient transaction routing without real-time parent lookups.

Findings

Reduces cross-shard communication overhead

Improves transaction processing throughput

Embeds parent-child relationships into model parameters

Abstract

This paper introduces a Machine Learning (ML) approach for scalability of UTXO-based blockchains, such as Bitcoin. Prior approaches to UTXO set sharding struggle with distributing UTXOs effectively across validators, creating substantial communication overhead due to child-parent transaction dependencies. This overhead, which arises from the need to locate parent UTXOs, significantly hampers transaction processing speeds. Our solution uses ML to optimize not only UTXO set sharding but also the routing of incoming transactions, ensuring that transactions are directed to shards containing their parent UTXOs. At the heart of our approach is a framework that combines contrastive and unsupervised learning to create an embedding space for transaction outputs. This embedding allows the model to group transaction outputs based on spending relationships, making it possible to route transactions efficiently to the correct validation microservices. Trained on historical transaction data with triplet loss and online semi-hard negative mining, the model embeds parent-child spending patterns directly into its parameters, thus eliminating the need for costly, real-time parent transaction lookups. This significantly reduces cross-shard communication overhead, boosting throughput and scalability.