Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …

In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Federated learning (FL) algorithm has been widely studied in recent years due to its ability
for sharing data while protecting privacy. However, FL has risks, such as model inversion …

Federated Learning (FL) offers collaborative machine learning without data exposure, but
challenges arise in the mobile edge network (MEC) environment due to limited resources …

Privacy preservation is a critical concern in Federated Learning (FL). However, traditional
Local Differential Privacy (LDP) methods face challenges in balancing FL model accuracy …

Federated learning (FL) when deployed in an Internet of Things (IoT) ecosystem can
facilitate the collaborative training of a global model involving different IoT local systems …

Federated learning (FL) is a distributed machine learning framework that enables the
training of shared models without the need to share local data. However, FL faces …

Federated Learning (FL) realizes distributed machine learning training via sharing model
updates rather than raw data, thus ensuring data privacy. However, an attacker may infer the …

Federated Learning (FL) in load forecasting improves predictive accuracy by leveraging
data from distributed load networks while preserving data privacy. However, the …