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.

199 is an important step toward creating next-generation smart grids that can effectively address the complex challenges of modern energy and other sectors. The implementation of CEAI in decentralized energy systems will significantly enhance their performance and resilience, promoting sustainable development and improving quality of life. The previously discussed principles of Collaborative Edge AI provide a foundation for understanding how this technology can be applied in practical systems, particularly in decentralized energy grids. Given the growing number of distributed energy sources, such as rooftop solar panels and local battery storage systems, it is critical to implement intelligent solutions to optimize their operation in real time. This is where Collaborative Edge AI plays a catalytic role, enabling effective coordination, forecasting, and management of energy flows. One of the key tasks is forecasting local energy consumption, accomplished using intelligent algorithms integrated directly into the edge devices. These algorithms analyze historical data and real-time conditions to predict future energy needs. This information facilitates more rational resources allocation, reducing loss and preventing inefficient energy use. Through federated learning, these forecasts can be refined collaboratively without compromising user privacy, as raw data remains local and only model updates are exchanged between devices. Dynamic interaction between devices is another key element. Devices within the grid exchange information and resources, enabling load balancing and avoiding overloads in local segments. For example, approaches based on the swarm computing concept facilitate the shared use of computational and energy resources among neighboring devices. This enhances grid resilience and allows for a more flexible response to changing operating conditions, ensuring continuous operation even in the event of local failures. An important aspect in such systems is the timely detection of faults and anomalies. Collaborative Edge AI simplifies and improves these processes through rapid information exchange between devices. For example, isolating peak overloads or detecting operational anomalies helps prevent cascading failures within the grid.