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 (PFL) is a subfield of federated learning. Contrary to
conventional federated learning that expects to find a general global model, PFL generates …

Federated learning (FL) is vulnerable to heterogeneously distributed data, since a common
global model in FL may not adapt to the heterogeneous data distribution of each user. To …

As a popular paradigm of distributed learning, personalized federated learning (PFL) allows
personalized models to improve generalization ability and robustness by utilizing …

Data heterogeneity is one of the main challenges faced by federated learning (FL). Unlike
traditional FL methods (eg FedAvg) which train a global model for all clients, personalized …

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 …

Federated learning is a distributed machine learning approach that allows a single server to
collaboratively build machine learning models with multiple clients without sharing datasets …

In Federated Learning (FL), the clients learn a single global model (FedAvg) through a
central aggregator. In this setting, the non-IID distribution of the data across clients restricts …

Federated learning (FL) is a well-established distributed machine-learning paradigm that
enables training global models on massively distributed data ie, training on multi-owner …

Motivated by high resource costs of centralized machine learning schemes as well as data
privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on …