Edge AI Deploying Artificial Intelligence Models on Edge Devices for Real-Time Analytics

Because of its on-the-go nature, edge AI has gained popularity, allowing for realtime analytics by deploying artificial intelligence models onto edge devices. Despite the promise of Edge AI evidenced by existing research, there are still significant barriers to widespread adoption with issues such as scalability, energy efficiency, security, and reduced model explainability representing common challenges. Hence, while this paper solves the Edge AI in a number of ways, with real use case of a deployment, modular adaptability, and dynamic AI model specialization. Our paradigm achieves low latency, better security and energy efficiency using light-weight AI models, federated learning, Explainable AI (XAI) and smart edge-cloud orchestration. This framework could enable generic AI beyond specific applications that depend on multi-modal data processing, which contributes to the generalization of applications across various industries such as healthcare, autonomous systems, smart cities, and cybersecurity. Moreover, this work will help deploy sustainable AI by employing green computing techniques to detect anomalies in near real-time in various critical domains helping to ease challenges of the modern world.