Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

Personalization in federated learning (FL) functions as a coordinator for clients with high
variance in data or behavior. Ensuring the convergence of these clients' models relies on …

Traditional federated learning addresses the data security issues arising from the need to
centralize client datasets on a central server for model training. However, this approach still …

Federated Learning (FL) seeks to train a model collaboratively without sharing private
training data from individual clients. Despite its promise, FL encounters challenges such as …

In practical scenarios, federated learning frequently necessitates training personalized
models for each client using heterogeneous data. This paper proposes a backbone self …