… In order to flexibly implement the aggregation of inconsistent user models in federated
learning, we propose the differentiated component aggregation strategy as Algorithm 2. With the …

… 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 …

… propose a personalization approach for federated learning and … Following the statistical
learning theory, in a federated learning … Intrinsically, as in federated learning, the global model is …

… for various users. In this paper, we study a personalized variant of the federated learning
in which our goal is to find an initial shared model that current or new users can easily adapt …

… for various users. In this paper, we study a personalized variant of the federated learning
in which our goal is to find an initial shared model that current or new users can easily adapt …

… 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 …

… personalized federated learning (pFedLA) training framework that can effectively exploit
the inter-user … among clients with non-IID data and produce accurate personalized models; …

… learning-theoretic study of personalization. We propose and analyze three approaches:
user clustering, data interpolation, and model … For all three approaches, we provide learning-…

… personalization aspects in federated learning by viewing deep learning models as base +
personalization … Our training algorithm comprises of the base layers being trained by federated …

… one of personalized federated learning and the results are instrumental to derive our user-…
In the federated learning setting, the weighted combination of the empirical loss terms of the …