  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.
