IEEE Consumer Communications & Networking Conference
10–13 January 2025 // Las Vegas, NV, USA



Recent research in academia and industry has increasingly focused on native networks,  which aim to integrate artificial intelligence (AI) into wireless  networks,prompting a deep exploration of their potential enhancements. Researchers have started to recognize the necessity for a well-defined AI governance framework based on the principles of decentralization and its natural alignment with wireless networks. This would eventually yield a human-like intelligence capable of real-time response, scalable, and adaptable to networkdynamicity while focusing on performance optimality and energy efficiency requirements. Our goal in organizing this workshop is to explore distributed-based intelligent communication network technologies to manage network resources smartly, enhance security and privacy, improve energyefficiency, and better understand the distributed nature of AI and its adaptation to wireless networks. We are soliciting high-quality articles on distributed AI-based communication technologies with original results and/or conceptual studies for this workshop. The aim is to bringout novel ideas, concepts, open challenges, or issues, from the researchers working in this topical and rapidly evolving domain.

The proposed workshop, Distributed Artificial Intelligence (DAINET – 1), aims to foster an environment conducive to the exchange of distributed intelligent solutions and future challenges for communication networks that yield autonomous decision-making capabilities across different network layers among attendees. Within this framework, the emphasis will also be on fortifying the participants’ understanding of the intricacies surrounding distributed AI, opening an amazing chance to collaborate by enhancing their networking with other fellows working in the field. The proceedings of the workshop will add great value to the scientific community focused on distributed AI solutions for networking challenges, contributing to the growth of human-like intelligence capable of real-time response, scalability, and adaptability to network dynamicity while focusing on performance optimality and energy efficiency requirements. Key areas of focus within this domain include network management, the enhancement of network security and privacy protocols, the optimization of AI agent communication across various networking paradigms, and more.

This workshop aims to bring together researchers, academics, and individuals working in selected areas of Native Networks, focusing on the applications of distributed machine learning techniques in network management, future networks including 5G/6G, sensor and IoT, smart cities, and more. We welcome contributions from both industry and academia to highlight and introduce solutions to the challenges associated with distributed intelligent communication network technologies.