Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios

Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios

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 privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively...

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Bibliographic Details
Main Authors: Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Batch normalization
epochs per round
federated averaging
federated learning
neural networks
non-IID data
Online Access:https://ieeexplore.ieee.org/document/10486903/

Internet

https://ieeexplore.ieee.org/document/10486903/

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