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 …

Federated learning (FL) is a distributed paradigm that coordinates massive local clients to
collaboratively train a global model via stage-wise local training processes on the …

The Mixture of Experts (MoE) has emerged as a highly successful technique in deep
learning, based on the principle of divide-and-conquer to maximize model capacity without …

Federated Learning (FL) is an emerging collaborative machine learning framework where
multiple clients train the global model without sharing their own datasets. In FL, the model …

Federated Learning (FL) aggregates locally trained models from individual clients to
construct a global model. While FL enables learning a model with data privacy it often …

Both centralized and decentralized approaches have shown excellent performance and
great application value in federated learning (FL). However, current studies do not provide …

Federated Learning (FL) models often experience client drift caused by heterogeneous data,
where the distribution of data differs across clients. To address this issue, advanced …

Federated learning (FL) is a distributed machine learning paradigm ensuring data privacy.
However, the statistical heterogeneity poses a challenge to building a single model that can …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated Learning (FL) aggregates locally trained models from individual clients to
construct a global model. While FL enables learning a model with data privacy, it often …