The example results are ResNet18 on different datasets using ER and ER+ID as baseline methods with different buffers and 0-4 seeds. All results reported here were ...

Abstract: Federated Learning is an approach that enables multiple devices to collectively train a shared model without sharing raw data, thereby preserving data privacy. However, federated learning ...

Abstract: Federated learning is presented as an effective solution to train artificial intelligence models on the Internet of Things networks without centralizing data, thus preserving privacy and ...

Abstract: Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try ...