Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …

Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …

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

Abstract Dynamic Facial Expression Recognition (DFER) is a rapidly develo** field that
focuses on recognizing facial expressions in video format. Previous research has …

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 …

Abstract One-shot Federated Learning (FL) has recently emerged as a promising approach,
which allows the central server to learn a model in a single communication round. Despite …

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

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 a promising paradigm that enables collaboratively learning a
shared model across massive clients while kee** the training data locally. However, for …