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              <p>Executing Artificial Intelligence (AI) on Internet of Things (IoT) devices is constrained by limited computation, memory, and energy resources. Techniques such as pruning, quantization, and compression enable lightweight deployment but often compromise model accuracy and responsiveness. Existing distributed AI frameworks also suffer from static workload mapping, bandwidth dependency, and orchestration overhead. To overcome these issues, this work introduces Granular AI, a paradigm that redefines AI deployment across the IoT-edge-fog-cloud continuum. A federated execution model enables adaptive placement, low-latency inference, and optimized resource utilization through an energy-aware scheduling framework. By aligning AI deployment with distributed systems principles, Granular AI and its Functionality-as-a-Service (FaaS) component enables Quality of Service (QoS)-aware deployments, achieving coordinated task placement, controlled execution, and resilient, scalable, real-time intelligence at the extreme edge of IoT ecosystems. A smart thermostat use case illustrates full model decomposition across a smart home setup.</p>
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