Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …

The advent of federated learning (FL) has sparked a new paradigm of parallel and
confidential decentralized machine learning (ML) with the potential of utilizing the …

Federated Learning is a novel framework that allows multiple devices or institutions to train a
machine learning model collaboratively while preserving their data private. This …

With the growing awareness of data privacy, federated learning (FL) has gained increasing
attention in recent years as a major paradigm for training models with privacy protection in …

In recent years, federated learning has received widespread attention as a technology to
solve the problem of data islands, and it has begun to be applied in fields such as finance …

Federated learning-assisted edge intelligence enables privacy protection in modern
intelligent services. However, not independent and identically distributed (non-IID) …

In the manufacturing process, equipment failure is directly related to productivity, so
predictive maintenance plays a very important role. Industrial parks are distributed, and data …

We propose FedEnhance, an unsupervised federated learning (FL) approach for speech
enhancement and separation with non-IID distributed data across multiple clients. We …

Machine learning has been widely used these days to detect novel intrusions across IoT
devices. Supervised-based machine learning techniques need labelled datasets to train a …

Wired and wireless communication data is getting bigger and bigger at such a high pace.
Accordingly, the big data (BD) communication networks should be developed as quickly as …