Cross-silo federated learning (FL) enables the development of machine learning models on
datasets distributed across data centers such as hospitals and clinical research laboratories …

Cross-silo Federated learning (FL) has become a promising tool in machine learning
applications for healthcare. It allows hospitals/institutions to train models with sufficient data …

Conventional federated learning (FL) approaches are ineffective in scenarios where clients
have significant differences in the distributions of their local data. The Non-IID data …

Conventional federated learning (FL) trains one global model for a federation of clients with
decentralized data, reducing the privacy risk of centralized training. However, the distribution …

In Federated Learning (FL), model aggregation is pivotal. It involves a global server
iteratively aggregating client local trained models in successive rounds without accessing …

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …

Federated learning (FL) allows multiple clients to collaboratively train a deep learning
model. One major challenge of FL is when data distribution is heterogeneous, ie, differs from …

Federated Learning (FL) is an evolving machine learning method in which multiple clients
participate in collaborative learning without sharing their data with each other and the …

Federated Learning (FL) is a machine learning technique that enables multiple local clients
holding individual datasets to collaboratively train a model, without exchanging the clients' …

Recently, various personalized federated learning (FL) algorithms have been proposed to
tackle data heterogeneity. To mitigate device heterogeneity, a common approach is to use …