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On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
April 30, 2024, 4:42 a.m. | Usevalad Milasheuski. Luca Barbieri, Bernardo Camajori Tedeschini, Monica Nicoli, Stefano Savazzi
cs.LG updates on arXiv.org arxiv.org
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
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