A First Application of Collaborative Learning In Particle Physics

TL;DR

This paper investigates the application of collaborative learning using blockchain technology to train machine learning models on neutrino physics simulation data, aiming to enable decentralized, multi-stakeholder model training in particle physics.

Contribution

It introduces the use of the Colearn library for decentralized collaborative learning in particle physics, demonstrating its feasibility with neutrino simulation datasets.

Findings

Successful decentralized training of a DL model with multiple datasets

Feasibility of collaborative learning in neutrino physics

Discussion of competitive model frameworks for community-wide optimization

Abstract

Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, including particle physics. The industry has also developed new powerful tools that, imported into academia, could revolutionise research. One recent industry development that has not yet come to the attention of the particle physics community is Collaborative Learning (CL), a framework that allows training the same ML model with different datasets. This work explores the potential of CL, testing the library Colearn with neutrino physics simulation. Colearn, developed by the British Cambridge-based firm Fetch.AI, enables decentralised machine learning tasks. Being a blockchain-mediated CL system, it allows multiple stakeholders to build a shared ML model without needing to rely on a central authority. A generic Liquid Argon Time-Projection Chamber (LArTPC) has been simulated and images produced by fictitious neutrino interactions have been used to produce several datasets. These datasets, called learners, participated successfully in training a Deep Learning (DL) Keras model using blockchain technologies in a decentralised way. This test explores the feasibility of training a single ML model using different simulation datasets coming from different research groups. In this work, we also discuss a framework that instead makes different ML models compete against each other on the same dataset. The final goal is then to train the most performant ML model across the entire scientific community for a given experiment, either using all of the datasets available or selecting the model which performs best among every model developed in the community.