Breast Cancer Detection and Prediction using Federated Multicriteria Machine Learning

Breast cancer is still one of the most common cancers in women, and it is also the leading cause of mortality among women. Breast cancer detection has been improved using a variety of Machine Learning and Deep Learning techniques. Federated Learning is a distributed learning technique that allows models to be trained on a vast set of decentralized data. In this paper we present a new Multiple Criteria Optimization approach for Federated Learning. We use Goal Programming in particular to address the Federated Learning problem's diverse set of objectives. Numerical experiments also demonstrate the efficacy of this strategy in terms of performance.