Analyzing the Role of Edge AI in Enabling Distributed Learning and Collaborative Intelligence in IoT

The rapid growth of Internet of Things (IoT) devices has generated massive amounts of data at the network edge, leading to challenges in processing and analyzing this data using traditional cloud-centric approaches. To address these challenges, the integration of Edge Artificial Intelligence (AI) has emerged as a promising solution. Edge AI refers to the integration of AI algorithms and models on edge devices, enabling real-time data processing and decision-making at the edge of the network. This paradigm shift brings numerous benefits, including reduced latency, improved security and privacy, enhanced scalability, and efficient resource utilization. The findings highlight the importance of edge AI in overcoming the limitations of traditional cloud-centric approaches and its potential impact on diverse domains. By leveraging the benefits of edge computing and distributed learning, organizations can enhance real-time data processing, collaborative decision-making, and advanced analytics while preserving data privacy and improving system performance. This article contributes to the existing knowledge on edge AI, distributed learning, and collaborative intelligence, providing guidance and insights for leveraging the potential of edge AI in IoT environments.

کلیدواژه ها:

Edge AI ، Distributed learning ، Collaborative intelligence ، IoT ، Edge computing