The challenge of personalized federated learning (pFL) is to capture the heterogeneity
properties of data with in-expensive communications and achieving customized …

Personalized Federated Learning (PFL) relies on collective data knowledge to build
customized models. However, non-IID data between clients poses significant challenges, as …

Recently, various personalized federated learning (FL) algorithms have been proposed to
tackle data heterogeneity. To mitigate device heterogeneity, a common approach is to use …

Personalized federated learning considers learning models unique to each client in a
heterogeneous network. The resulting client-specific models have been purported to …

Federated learning is a promising approach for training models on distributed data, driven
by increasing demand in various industries. However, federated learning framework faces …

Due to reasons such as non-iid data distribution, the generation of a single global model
through collaborative distributed clients often fails to meet the task requirements of individual …

As a privacy-preserving collaborative machine learning paradigm, federated learning (FL)
has attracted significant interest from academia and the industry alike. To allow each data …

Facing the challenge of statistical diversity in client local data distribution, personalized
federated learning (PFL) has become a growing research hotspot. Although the state-of-the …

We present PeFLL, a new personalized federated learning algorithm that improves over the
state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low …

Federated learning (FL) is a novel machine learning that performs distributed training locally
on devices and aggregating the local models into a global one. The limited network …