Proceedings of the International scientific and practical conference ―Science, Technology and Culture in the Era of Globalization‖ (December 24-26, 2025) / Publisher website: www.naukainfo.com. – Geneva, Switzerland, 2026. – 234 p.

198 Therefore, the integration of decentralized energy grids and intelligent control systems based on Edge AI represents an important step toward creating more resilient, adaptive, and efficient energy systems. This requires increased attention from researchers, practitioners, and policymakers, as well as investment into the development of relevant technologies. Such a comprehensive approach opens up prospects for enhancing energy resilience and environmental safety on a global scale. However, as the number and diversity of devices grows, the need for closer collaboration between them arises, enabling information sharing and collaborative learning. This is where the concept of Collaborative Edge AI (CEAI) comes into play, opening new horizons in decentralized intelligent systems. Collaborative Edge AI is an approach that combines distributed computational resources and artificial intelligence, with an emphasis on interaction and collaboration between devices at the edge of the grid. It enables local devices not only to process data but also to efficiently exchange information and jointly train AI models, while keeping data locally and ensuring their confidentiality. The key mechanism enabling collaboration in CEAI is federated learning—a method that allows devices to train shared models using local data without transmitting it centrally. This ensures the protection of user privacy while simultaneously improving artificial intelligence performance by leveraging diverse distributed data sources. In turn, device interaction via peer-to-peer networks facilitates the exchange of computational resources and knowledge, thereby enhancing the system`s adaptability and efficiency. CEAI differs from traditional Edge AI in its focus on shared intelligence and collaboration between devices, rather than the autonomous operation of individual nodes. Due to mechanisms of dynamic group formation and task allocation, CEAI can quickly adapt to changing operating conditions and data processing requirements. This is especially important for applications that require minimal latency and high reliability, such as medical diagnostic systems or intelligent transportation. Thus, Collaborative Edge AI opens up new possibilities for the development of distributed systems, ensuring their flexibility, scalability, and security. Its application