For modern artificial intelligence (AI) applications such as large language models (LLMs),
the training paradigm has recently shifted to pre-training followed by fine-tuning …

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

While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT
data features, the data diversity (eg, the heterogeneity and high dimensions of data) also …

Due to the progress in computer hardware and network technologies, federated learning
(FL), a decentralized training method in machine learning, has garnered widespread …

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

Personalized federated learning (PFL) has emerged as a promising technique for
addressing the challenge of data heterogeneity. While recent studies have made notable …

In Personalized Federated Learning (PFL), existing methods often overlook the intricate
interconnections between clients and their local datasets, limiting effective information …