Technically, this benefit can be realized by implementing multi-task federated learning. Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time while exploiting commonalities and differences across tasks. In this case, the multiple tasks are drug discovery tasks on compound data sets owned by different pharmaceutical companies.
In multi-task federated learning, the machine learning model travels from one data center to another, while the data is not shared and remains in the data center of the specific pharmaceutical company. However, the raw dataset used for training contains private information, which can be maliciously recovered by carefully analyzing the model and outputs. To preserve privacy, the machine learning model is made of a common trunk shared between partners, while private heads are not shared among partners. The multi-task predictive machine learning algorithms incorporating an extended privacy management system, to identify the most effective compounds for drug development, while protecting the intellectual property rights of the consortium contributors.