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 …

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 …

Conventional federated learning (FL) approaches are ineffective in scenarios where clients
have significant differences in the distributions of their local data. The Non-IID data …

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 …

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 (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

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

This work addresses the problem of optimizing communications between server and clients
in federated learning (FL). Current sampling approaches in FL are either biased, or non …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

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 …