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

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.