The origin of the Non-IID phenomenon is the personalization of users, who generate the Non-IID data. With Non-IID (Not Independent and Identically Distributed) issues existing in the federated learning setting, a myriad of approaches has been proposed to
The origin of thestatistical heterogeneityphenomenon is the personalization of users, who generate the non-IID (not Independent and Identically Distributed) and unbalanced data. With statistical heterogeneity existing in the FL scenario, a myriad of approaches have been proposed to crack this hard nut...
Personalized FL (pFL) FedMTL (not MOCHA)— Federated multi-task learning NeurIPS 2017 FedBN— FedBN: Federated Learning on non-IID Features via Local Batch Normalization ICLR 2021 Meta-learning-based pFL Per-FedAvg— Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Me...
PFL-DA—Personalized Federated Learning via Domain Adaptation with an Application to Distributed 3D PrintingTechnometrics 2023 Other pFL FedMTL (not MOCHA)—Federated multi-task learningNeurIPS 2017 FedBN—FedBN: Federated Learning on non-IID Features via Local Batch NormalizationICLR 2021 ...
Personalized federated learning simulation platform with non-IID and unbalanced dataset - LEON-gittech/PFLlib