Log In Sign Up

Reward Systems for Trustworthy Medical Federated Learning

by   Konstantin D. Pandl, et al.

Federated learning (FL) has received high interest from researchers and practitioners to train machine learning (ML) models for healthcare. Ensuring the trustworthiness of these models is essential. Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models. In this research, we address the question to which extent bias occurs in medical FL and how to prevent excessive bias through reward systems. We first evaluate how to measure the contributions of institutions toward predictive performance and bias in cross-silo medical FL with a Shapley value approximation method. In a second step, we design different reward systems incentivizing contributions toward high predictive performance or low bias. We then propose a combined reward system that incentivizes contributions toward both. We evaluate our work using multiple medical chest X-ray datasets focusing on patient subgroups defined by patient sex and age. Our results show that we can successfully measure contributions toward bias, and an integrated reward system successfully incentivizes contributions toward a well-performing model with low bias. While the partitioning of scans only slightly influences the overall bias, institutions with data predominantly from one subgroup introduce a favorable bias for this subgroup. Our results indicate that reward systems, which focus on predictive performance only, can transfer model bias against patients to an institutional level. Our work helps researchers and practitioners design reward systems for FL with well-aligned incentives for trustworthy ML.


page 1

page 6

page 7


Mitigating Bias in Federated Learning

As methods to create discrimination-aware models develop, they focus on ...

Towards the Practical Utility of Federated Learning in the Medical Domain

Federated learning (FL) is an active area of research. One of the most s...

Federated learning for violence incident prediction in a simulated cross-institutional psychiatric setting

Inpatient violence is a common and severe problem within psychiatry. Kno...

Application of Federated Learning in Building a Robust COVID-19 Chest X-ray Classification Model

While developing artificial intelligence (AI)-based algorithms to solve ...

OpenFL: An open-source framework for Federated Learning

Federated learning (FL) is a computational paradigm that enables organiz...

FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction

Graph Convolutional Neural Networks (GCNs) are widely used for graph ana...

Rethinking Defaults Values: a Low Cost and Efficient Strategy to Define Hyperparameters

Machine Learning (ML) algorithms have been successfully employed by a va...