Evaluating Federated Learning Scenarios in a Tumor Classification Application

Abstract

Federated Learning is a new area of distributed Machine Learning (ML) that emerged to deal with data privacy concerns. In this approach, each client has access to a local and private dataset. They only exchange the model weights and updates. This paper presents a Federated Learning (FL) approach to a cloud Tumor-Infiltrating Lymphocytes (TIL) application. The results show that the FL approach outperformed the centralized one in all evaluated ML metrics. It also reduced the execution time although the financial cost has increased.