Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios
Bruno Casella,
Roberto Esposito,
Antonio Sciarappa
et al.
Abstract:Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacypreserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical performance challenge of FL is operating on datasets not independently and identically distributed (non-IID) among the federation participants. Even though t… Show more
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