In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Data mining is a process to extract unknown, hidden, and potentially useful information from
data. But the problem of data island makes it arduous for people to collect and analyze …

Distributed learning algorithms aim to leverage distributed and diverse data stored at users'
devices to learn a global phenomena by performing training amongst participating devices …

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm,
drawing tremendous attention in both academia and industry. A unique characteristic of FL …

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 (FL) is a promising paradigm that enables collaboratively learning a
shared model across massive clients while keeping the training data locally. However, for …

The increasing data produced by IoT devices and the need to harness intelligence in our
environments impose the shift of computing and intelligence at the edge, leading to a novel …

Federated learning (FL) becomes a promising machine learning paradigm. The impact of
heterogeneous hardware specifications and dynamic states on the FL process has not yet …

Federated learning (FL) enables collaborative model training without centralizing data.
However, the traditional FL framework is cloud-based and suffers from high communication …

Conducting federated learning across distributed sites with In-Band Network Telemetry (INT)
based data collection faces critical challenges, including control decisions of different …