… for training a single global model over decentralized data. … federated learning from a
client-centric or personalized perspective. We aim to enable stronger performance on personalized …

… efficient optimization algorithm that adaptively learns the model … a personalization approach
for federated learning and … Following the statistical learning theory, in a federated learning …

… To address this, we propose an algorithm for personalized FL (pFedMe) … personalized
model optimization from the global model learning in a bi-level problem stylized for personalized …

… optimization aspects of personalized Federated Learning (FL). We propose general optimizers
that can be applied to numerous existing personalized … a general personalized objective …

… is the first federated learning paradigm that realizes personalized model training via …
Insert (2) into (4), and we can design an alternating optimization approach to solve (4), that …

… is already a meta learning algorithm, optimizing for personalized performance, as opposed
… context of Federated Learning, the accuracy of the global model after personalization should …

… learning models on data silos in a privacy-preserving manner. In this survey, we explore the
domain of personalized … Here, we provide formulations of the optimization objectives under …

… on the personalized federated learning problem in terms of not only client-distinct models, but
… To better account for the data heterogeneity and system heterogeneity, we aim to optimize …

… lagged model convergence and inadequate personalization … Layer-wised Personalized
Federated learning (pFedLA) that … is able to optimize the personalized model aggregation for …

… explores various model architectures commonly utilized in Personalized Federated Learning
(… architectures, advantages, and algorithms related to PFL model optimization. Effective PFL …