In this paper, we propose an independent two-stage personalized FL framework, ie, Fed-RepPer, to separate representation learning from classification in ...
Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical heterogeneity ...
Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical heterogeneity ...
Jun 1, 2023 · Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical ...
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My research explores the possibility of personalized FL architecture and FL aggregation algorithms to achieve personalized learning objectives for each client ...
Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client.
In this paper, we consider the use of recent FL schemes to train a set of personalized models that are then optimally fused through Bayesian rules, which makes ...
Jun 28, 2024 · This paper aims to design a novel FL method to robust and interpret the non-IID data across clients. Specifically, we interpret each client's ...
Jul 15, 2024 · In this work, we propose a Group-based Federated Meta-Learning framework, called G-FML , which adaptively divides the clients into groups based ...
The origin of the statistical heterogeneity phenomenon is the personalization of users, who generate non-IID (not Independent and Identically Distributed) and ...