Asynchronous Collaborative Learning Across Data Silos
This addresses data fragmentation issues in regulated industries like finance or healthcare, but is incremental as it builds on existing federated learning techniques.
The paper tackles the problem of training machine learning models across fragmented data silos within organizations, proposing a framework for asynchronous collaborative training without data sharing, and validates it through extensive experiments.
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.