… (FL) has shown great promise in recent years for training a … federated learning from a
client-centric or personalized perspective. We aim to enable stronger performance on personalized …

… In Federated Learning, we aim to train models across … , we study a personalized variant of
the federated learning in which … all the benefits of the federated learning architecture, and, by …

… In Federated Learning, we aim to train models across … , we study a personalized variant of
the federated learning in which … all the benefits of the federated learning architecture, and, by …

… In this section, we propose a personalization approach for federated learning and analyze
its statistical properties. Following the statistical learning theory, in a federated learning setting …

… The abundance of data generated in a massive number of hand-held devices these days
has stimulated the development of Federated learning (FL) [1]. The setting of FL is a network of …

… Furthermore, to quantify the contributions of each client to others’ personalized training,
the … is the first federated learning paradigm that realizes personalized model training via …

… In the context of personalized federated learning (FL), the critical challenge is to balance …
-aware personalized FL method where each client can automatically balance the training of its …

… user models in federated learning also requires a careful design. To address the above
challenges, we propose a novel Hierarchical Personalized Federated Learning (HPFL) …

… of Federated Learning (FL), the leading paradigm for the training of machine learning models
… In this survey, we explore the domain of personalized FL (PFL) to address the fundamental …

… Federated learning allows clients to collaboratively learn statistical models while keeping their
data local. Federated learning … order to tackle this limitation, recent personalized federated …