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Document type:
Zeitschriftenaufsatz 
Author(s):
Yeganeh, Y.; Farshad, A.; Navab, N.; Albarqouni, S. 
Title:
Inverse Distance Aggregation for Federated Learning with Non-IID Data 
Abstract:
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distan...    »
 
Keywords:
Federated Learning,Distributed Learning,Privacy-preserving,Heterogeneous Data,Robustness 
Journal title:
arXiv preprint arXiv:2008.07665 
Year:
2020