Description: Model fusion is a process that combines multiple machine learning models into a single model, aiming to leverage the strengths of each and improve the accuracy and robustness of predictions. This approach is particularly relevant in contexts such as distributed learning and edge inference, where data may be scarce or distributed. Model fusion allows for the integration of different perspectives and characteristics of the data, resulting in a more generalizable and efficient model. Additionally, this process can help mitigate issues like overfitting, as combining models can balance the weaknesses of each. Fusion techniques may include methods such as ensemble learning, where predictions from several models are combined, or knowledge transfer, where one model is trained on one dataset and adjusted for use on another. In a world where data diversity and the need for customized solutions are increasingly important, model fusion emerges as a key strategy for optimizing the performance of artificial intelligence systems.
