Federated learning has been widely applied as a distributed machine learning method in
various fields, allowing a global model to be trained by sharing local gradients instead of …

Federated learning is an innovative machine learning technique that deals with centralized
data storage issues while maintaining privacy and security. It involves constructing machine …

Mobile Ad Hoc Networks (MANETs) are dynamic networks without fixed infrastructure,
making them particularly vulnerable to security threats such as black and gray hole attacks …

Federated learning (FL) allows clients to keep local data sets and train collaboratively by
uploading model gradients, which achieves the goal of learning from fragmented sensitive …

Abstract The establishment of Vehicular Ad Hoc Networks (VANETs) has brought significant
advantages to humans, yet it also raises crucial safety considerations. Security is one of the …

Mobile ad hoc networks (MANETs) face significant challenges in maintaining secure and
efficient communication owing to their dynamic nature and vulnerability to security threats …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning is a decentralized machine learning technique that creates a global
model by aggregating local models from multiple edge devices without a need to access the …

Federated Learning is a distributed machine learning method that offers inherent
advantages in efficient learning and privacy protection within edge computing scenarios …

Consumer electronics play a crucial role in the automotive sector by providing the essential
hardware infrastructure necessary for develo** autonomous vehicles. These vehicles …