Federated learning (FL) is a promising distributed learning paradigm in which multiple edge
devices (EDs) collaborate to train a shared model without exchanging privacy-sensitive raw …

In this paper, to deal with the heterogeneity in federated learning (FL) systems, a knowledge
distillation (KD) driven training framework for FL is proposed, where each user can select its …

Federated Learning (FL) has emerged as a promising approach to enable collaborative
learning among multiple clients while preserving data privacy. However, cross-domain FL …

Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm
that coordinates multiple clients to train a model while keeping the raw data localized …

Recently, innovative model aggregation methods based on knowledge distillation (KD) have
been proposed for federated learning (FL). These methods not only improved the …

Federated learning (FL) is able to manage edge devices to cooperatively train a model while
maintaining the training data local and private. One common assumption in FL is that all …

As a promising distributed machine learning paradigm, federated learning (FL) trains a
central model with decentralized data without compromising user privacy, which makes it …

Federated Learning (FL) has emerged as a promising collaborative learning paradigm that
enables to train machine learning models across decentralized devices, while keeping the …

Federated learning (FL) is a privacy-preserving machine learning paradigm in which the
server periodically aggregates local model parameters from cli ents without assembling their …

Federated learning (FL) is a distributed machine learning paradigm under privacy
preservation. However, data heterogeneity among clients leads to the shared global model …