Personalized federated learning (PFL) reduces the impact of non-independent and
identically distributed (non-IID) data among clients by allowing each client to train a …

Ensuring fairness is a crucial aspect of Federated Learning (FL), which enables the model to
perform consistently across all clients. However, designing an FL algorithm that …

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in
machine learning training. This approach allows a variety of mobile devices to …

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

To address data heterogeneity, the key strategy of Personalized Federated Learning (PFL)
is to decouple general knowledge (shared among clients) and client-specific knowledge, as …

Personalized Federated Learning (PFL) is a commonly used framework that allows clients to
collaboratively train their personalized models. PFL is particularly useful for handling …

In traditional Federated Learning approaches like FedAvg, the global model underperforms
when faced with data heterogeneity. Personalized Federated Learning (PFL) enables clients …

Because of the effectiveness of federated learning in protecting privacy and breaking the"
data silo" phenomenon, it has been widely studied and applied in recent years. Firstly, the …