  Machine learning algorithms can perform well when trained on large datasets.
While large organisations often have considerable data assets, it can be
difficult for these assets to be unified in a manner that makes training
possible. Data is very often 'siloed' in different parts of the organisation,
with little to no access between silos. This fragmentation of data assets is
especially prevalent in heavily regulated industries like financial services or
healthcare. In this paper we propose a framework to enable asynchronous
collaborative training of machine learning models across data silos. This
allows data science teams to collaboratively train a machine learning model,
without sharing data with one another. Our proposed approach enhances
conventional federated learning techniques to make them suitable for this
asynchronous training in this intra-organisation, cross-silo setting. We
validate our proposed approach via extensive experiments.
