Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Split learning (SL) is a collaborative learning framework, which can train an artificial
intelligence (AI) model between a device and an edge server by splitting the AI model into a …

Federated learning (FL) has been considered as a promising paradigm for enabling
distributed training/learning in many machine-learning services without revealing users' …

In this paper, the problem of wireless resource allocation and semantic information
extraction for energy efficient semantic communications over wireless networks with rate …

Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …

Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …