Federated learning suffers from a latency bottleneck induced by network stragglers, which
hampers the training efficiency significantly. In addition, due to the heterogeneous data …

Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …

As a practical paradigm designed to involve large numbers of edge devices in distributed
training of deep learning models, federated learning has witnessed a significant amount of …

Federated Learning (FL) has achieved significant achievements recently, enabling
collaborative model training on distributed data over edge devices. Iterative gradient or …

FedLab is a lightweight open-source framework for the simulation of federated learning. The
design of FedLab focuses on federated learning algorithm effectiveness and communication …

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) enables collaborations among clients for train machine learning
models while protecting their data privacy. Existing FL simulation platforms that are …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

Federated learning (FL) inevitably confronts the challenge of system heterogeneity in
practical scenarios. To enhance the capabilities of most model-homogeneous FL methods in …

Federated Learning (FL) has been widely accepted as the solution for privacy-preserving
machine learning without collecting raw data. While new technologies proposed in the past …