AZ Tan, H Yu, L Cui, Q Yang - … on neural networks and learning …, 2022 - ieeexplore.ieee.org
… of FederatedLearning (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 …
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 …
… Federatedlearning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. … to other federatedlearning …
… 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 …
… In this section, we propose a personalization approach for federatedlearning and analyze its statistical properties. Following the statistical learning theory, in a federatedlearning setting …
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 …
… Federatedlearning (FL) has shown great promise in recent … federatedlearning from a client-centric or personalized perspective. We aim to enable stronger performance on personalized …
… The goal is to train personalized models collaboratively … pFedHN for personalized Federated HyperNetworks. In this ap… in several personalizedfederatedlearning challenges and find …
R Hu, Y Guo, H Li, Q Pei, Y Gong - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
… federatedlearning scheme for collaboratively training multiple personalized machine learning … a distributed learning scheme to achieve personalizedfederatedlearning with differential …