FedBN: Federated Learning on Non-IID Features via Local Batch
Normalization
FedBN: Federated Learning on Non-IID Features via Local Batch
Normalization
The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy. In most cases, the assumption of independent and identically distributed samples across local clients does not hold for federated learning setups. …