On the Trade-off Between Benefit and Contribution for Clients in Federated Learning in Healthcare

Abstract

Federated Learning (FL) is a learning paradigm that allows clients to profit from the data that is available across multiple clients to train a joint model. As FL allows to train such a joint model without explicitly sharing data, but only sharing model updates, it has attained popularity in healthcare settings where patient data is subject to strict privacy policies and needs to be locally stored at each hospital or healthcare provider. A particular challenge for FL settings is data imbalance across clients, as it has been found to be detrimental to model performance and impact the influence of each client on the learning process. Unfortunately, the healthcare domain is particularly prone to such imbalanced data due to regional differences in disease management, prescription behavior etc. In this paper, we introduce the two novel metrics Benefit and Contribution to quantify to which degree individual clients benefit from participation in FL and how they contribute to its success, respectively. Therefore, we measure Benefit and Contribution with respect to four types of imbalances present in data at each client side. Our results show that both client Benefit and Contribution are influenced by data imbalance in such a way that high imbalance in data quantity, label distribution and feature distribution reduces or nullifies clients’ Benefit while increasing their Contribution. Thus, the most valuable clients within a cohort benefit the least from their participation, exposing a critical thread to the success of clinical FL cohorts by withdrawing participation.