Agent-based models (ABMs) are one of the most effective and successful methods for
analyzing real-world complex systems by investigating how modeling interactions on the …

Federated Learning (FL) is a privacy-preserving way to utilize the sensitive data generated
by smart sensors of user devices, where a central parameter server (PS) coordinates …

Federated Learning (FL) improves the performance of the training phase of machine
learning procedures by distributing the model training to a set of clients and recombining the …

One of the main advancements in distributed learning may be the idea behind Google's
Federated Learning (FL) algorithm. It trains copies of artificial neural networks (ANN) in a …

One of the main advancements in distributed learning may be the idea behind Google's
Federated Learning (FL) algorithm. It allows a distributed deep learning process being made …

In this paper, we propose a multi-agent-based architecture for a Unity3D simulation of
dynamic agrirobot-fleet-coordination methods. The architecture is based on a Robot …

Federated learning (FL) has been proposed as a machine learning approach to
collaboratively learn a shared prediction model. Although, during FL training, only a subset …

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and
topics related to these fields are addressed separately or within the scope of AI. Multi-agent …

Multiagent systems promote a decentralized and distributed approach that enable the
division of complex problems into smaller parts. The use of multiagent systems also enables …

The increasing complexity in product development and the lack of knowledge exchange, for
example, between development and manufacturing lead to unnecessary iteration loops and …