Specific Model

Research on efficient and adaptable machine learning models focuses on optimizing inference speed and resource utilization while maintaining accuracy. Current efforts explore cascaded ensembles, which route data through models of varying complexity based on individual data characteristics, and federated learning frameworks tailored to specific institutions or resource-constrained environments, often employing neural architecture search to customize model architectures. These advancements aim to improve the practicality and scalability of machine learning, particularly in resource-limited settings like edge devices and distributed healthcare systems, leading to more efficient and cost-effective deployments.