Optimal Data

Optimal data research focuses on minimizing data requirements while maximizing performance in machine learning applications, addressing challenges like resource constraints and data acquisition costs. Current efforts explore efficient data representation techniques, including data reduction strategies (e.g., downsampling, quantization), optimal data exchange mechanisms in federated learning (comparing raw data, synthetic data, or model updates), and leveraging pre-trained models to generate synthetic data for faster training. These advancements are significant for improving the efficiency and scalability of machine learning across diverse domains, from resource-constrained IoT devices to large-scale collaborative learning systems, ultimately enabling broader access to data-driven insights.