Abstract

Recent advances in Artificial Intelligence (AI) and the increasing availability of computational power have accelerated the diffusion of Intelligent Cyber-Physical Systems (ICPSs), enabling smart applications with reasoning capabilities. However, the limited resources of embedded and Edge devices significantly constrain the complexity of deep learning models that can be effectively deployed. Traditional approaches rely on cloud-based training and edge-only inference, a paradigm that becomes inadequate when low latency, privacy, security, and high customization are required. In this context, On-device AI is emerging as a new paradigm in which both training and inference are performed directly on the device, avoiding data transfer and enabling faster, more energy-efficient, and privacy-preserving intelligent systems. Despite the challenges associated with resource constraints, the benefits of this approach motivate the exploration of novel architectures, frameworks, and methodologies. Additionally, as edge devices increasingly handle sensitive data, security and privacy considerations become fundamental aspects of system design. In this special issue we invited high-quality research on On-device AI solutions for Smart Environments and Industry 4.0 applications. Overall, 20 papers were accepted covering a wide range of areas.

Department(s)

Computer Science

Publication Status

Full Text Access

Keywords and Phrases

Deep Learning; Edge computing; Federated Learning; ICPS; IoT; On-device AI

International Standard Serial Number (ISSN)

0167-739X

Document Type

Editorial

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Elsevier, All rights reserved.

Publication Date

01 Jan 2026

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