… In this section, we propose a personalization approach for federatedlearning and analyze its statistical properties. Following the statistical learning theory, in a federatedlearning setting …
… success of centralized deep learning suggests that data often … federatedlearning framework and algorithm for learning a … over alternative personalizedfederatedlearning approaches …
… 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 …
… Federatedlearning (FL) has shown great promise in recent … federatedlearning from a client-centric or personalized perspective. We aim to enable stronger performance on personalized …
… In FederatedLearning, we aim to train models across … , we study a personalized variant of the federatedlearning in which … all the benefits of the federatedlearning architecture, and, by …
… personalization aspects in federatedlearning by viewing deep learning models as base + personalization … Our training algorithm comprises of the base layers being trained by federated …
J Zhang, S Guo, X Ma, H Wang… - Advances in Neural …, 2021 - proceedings.neurips.cc
… KT-pFL updates the personalized soft prediction of each … of each client to others’ personalized training, the knowledge … the first federatedlearning paradigm that realizes personalized …
A Fallah, A Mokhtari… - Advances in neural …, 2020 - proceedings.neurips.cc
… In FederatedLearning, we aim to train models across … , we study a personalized variant of the federatedlearning in which … all the benefits of the federatedlearning architecture, and, by …
X Ma, J Zhang, S Guo, W Xu - Proceedings of the IEEE/CVF …, 2022 - openaccess.thecvf.com
… personalizedfederatedlearning (pFedLA) training framework that can effectively exploit the inter-user similarities among clients with non-IID data and produce accurate personalized …