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

We propose a novel method for federated learning that is customized specifically to the
objective of a given edge device. In our proposed method, a server trains a global meta …

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared
model without exposing their raw data. However, heterogeneous devices usually have …

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 …

An oft-cited challenge of federated learning is the presence of heterogeneity.\emph {Data
heterogeneity} refers to the fact that data from different clients may follow very different …

Personalized federated learning (FL) aims to train model (s) that can perform well for
individual clients that are highly data and system heterogeneous. Most work in personalized …

Although remarkable progress has been made by existing federated learning (FL) platforms
to provide infrastructures for development, these platforms may not well tackle the …

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 …

This paper focuses on addressing the practical yet challenging problem of model
heterogeneity in federated learning, where clients possess models with different network …

Personalized federated learning is aimed at allowing numerous clients to train personalized
models while participating in collaborative training in a communication-efficient manner …