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 Cooking Wine a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained