Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

This work considers the category distribution heterogeneity in federated learning. This issue
is due to biased labeling preferences at multiple clients and is a typical setting of data …

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning
capabilities. Recently, personalized FL (pFL) has received attention for its ability to address …

This paper focuses on an under-explored yet important problem: Federated Class-Continual
Learning (FCCL), where new classes are dynamically added in federated learning. Existing …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

One of the key challenges in federated learning (FL) is local data distribution heterogeneity
across clients, which may cause inconsistent feature spaces across clients. To address this …

Federated learning (FL) is a distributed machine learning paradigm that enables data
owners to collaborate on training models while preserving data privacy. As FL effectively …

Federated Learning (FL) aims to learn a global model from distributed users while protecting
their privacy. However, when data are distributed heterogeneously the learning process …