The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …

In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor
data, an increasing number of actuators, and data-intensive algorithms, the development of …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

To overcome the challenge of limited bandwidth, client selection has been considered an
effective method for optimizing Federated Learning (FL). However, since the volatility of the …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

Federated Learning (FL) has gained significant attention because of its potential for privacy-
preserving distributed learning. However, statistical heterogeneity and label scarcity remain …

With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …

Federated Learning (FL) enables potentially a large number of clients to collaboratively train
a global model with the coordination of a central cloud server without exposing client raw …