On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks

arXiv:2404.18519v1 Announce Type: new
Abstract: Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information. One of those domains is healthcare, where groups of silos collaborate in order to generate a global predictor with improved accuracy and generalization. However, the inherent challenge lies in the high heterogeneity of medical data, necessitating sophisticated techniques for assessment and compensation. This paper presents a comprehensive exploration of the mathematical formalization and …

abstract application applications arxiv construction cs.ai cs.lg data dataset domains environments federated learning generate global healthcare impact information multiple networks privacy the information type