Deep neural networks have shown the ability to extract universal feature representations
from data such as images and text that have been useful for a variety of learning tasks …

Federated learning allows clients to collaboratively learn statistical models while keeping
their data local. Federated learning was originally used to train a unique global model to be …

In real-world federated learning scenarios, participants could have their own personalized
labels incompatible with those from other clients, due to using different label permutations or …

Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …

Personalized federated learning (PFL) has emerged as a paradigm to provide a
personalized model that can fit the local data distribution of each client. One natural choice …

While federated learning traditionally aims to train a single global model across
decentralized local datasets, one model may not always be ideal for all participating clients …

In recent years, personalized federated learning (pFL) has attracted increasing attention for
its potential in dealing with statistical heterogeneity among clients. However, the state-of-the …

Data heterogeneity is one of the most challenging issues in federated learning, which
motivates a variety of approaches to learn personalized models for participating clients. One …

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy
protection, collaborative learning, and tackling statistical heterogeneity among clients, eg …

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