The article compares different methods in machine learning, particularly focusing on DPFL and PPFL approaches. It highlights that PPFL (predefined physical model-based federated learning) showcases significantly higher computational efficiency due to its absence of training processes, in contrast to DPFL (data-driven physical model-based federated learning). The evaluations conducted confirm this advantage, pointing to opportunities for improved methodologies in machine learning, especially when managing vast data and ensuring efficiency in computational tasks.
The study shows that PPFL methods exhibit superior computational efficiency over DPFL methods due to their reliance on predefined physical models, avoiding training processes.
In evaluations, PPFL outperformed DPFL methods, emphasizing the need for advanced methodologies in machine learning to enhance efficiency in computational tasks.
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