Model-Free-Communication Federated Learning: Framework and application to Precision Medicine

The problem of executing machine learning algorithms over data while complying with data privacy is highly relevant in many application areas, including medicine in general and Precision Medicine in particular. In this paper, an innovative framework for Federated Learning is proposed that allows performing machine learning and effectively tackling the issue of data privacy while taking a step towards security during communication. Unlike the standard federated approaches where models should travel on the communication networks and would be subject to possible cyberattacks, the models proposed by our framework do not need to travel, thus moving in the direction of security improvement. Another very appealing feature is that it can be used with any machine learning algorithm provided that, during the learning phase, the model updating does not depend on the input data. To show its effectiveness, the learning process is here accomplished by an Evolutionary Algorithm, namely Grammatical Evolution, thus also obtaining explicit knowledge that can be provided to the domain experts to justify the decisions made. As a test case, glucose values prediction for a number of patients with type 1 diabetes is considered and is tackled as a classification problem, the goal being to predict for any future value a possible range. Finally, a comparison of the performance of the proposed framework is performed against that of a non-Federated Learning approach.