With decentralized data collected from diverse clients, a personalized federated learning
paradigm has been proposed for training machine learning models without exchanging raw …

Federated Learning (FL) is a collaborative machine learning technique to train a global
model without obtaining clients' private data. The main challenges in FL are statistical …

Personalized federated learning aims to address data heterogeneity across local clients in
federated learning. However, current methods blindly incorporate either full model …

Federated Learning (FL) offers a novel distributed machine learning context whereby a
global model is collaboratively learned through edge devices without violating data privacy …

For the challenging computational environment of IOT/edge computing, personalized
federated learning allows every client to train a strong personalized cloud model by …

In practical applications, federated learning (FL) suffers from slow convergence rate and
inferior performance resulting from the statistical heterogeneity of distributed data …

Federated learning (FL) is a collaborative machine learning technique to train a global
model (GM) without obtaining clients' private data. The main challenges in FL are statistical …

Personalized federated learning trains personalized models tailored to meet individual
client's specific data distributions. However, global models often introduce irrelevant …

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) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …