IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 435 10.51583/IJLTEMAS.2026.150900084 Machine Learning Edge AI: Privacy-Preserving Intelligence at the Source S Madhu Anoodha Department of Computer Science, Bishop Jerome Institute Kollam, Kerala, India India A R. Dr.Anil Department of Computer Science, Bishop Jerome Institute Kollam, Kerala, India India 15 9 2094 2099 26 09 2026 01 10 2026 15 10 2026 Edge AI Low Latency Real-Time Inference Resource-Constrained Devices Federated Learning. Abstract

Artificial Intelligence (AI) has traditionally relied on cloud-based computation for training and inference. While Cloud AI offers scalability and centralized resources, it suffers from latency, privacy risks, and dependency on internet connectivity. In contrast, Edge AI enables intelligence directly at the source — on devices such as smartphones, IoT sensors, and autonomous vehicles. This paper explores the limitations of Cloud AI, highlights the advantages of Edge AI, presents its layered architecture, discusses optimization techniques, and examines applications, challenges, results, and future scope.

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