Description: Model aggregation is a process that involves combining multiple machine learning models to improve the overall performance of predictions. This approach is based on the premise that by merging the outputs of several models, individual errors can be mitigated, and the strengths of each can be leveraged. There are various techniques to carry out this aggregation, such as ‘bagging’, which reduces variance by averaging the predictions of several models trained on different subsets of data, and ‘boosting’, which focuses on correcting the errors of previous models by adjusting the weight of misclassified instances. Model aggregation not only improves accuracy but can also enhance the robustness and generalization of the final model, making it less susceptible to overfitting. This approach is particularly relevant in contexts where data variability is high or where high prediction accuracy is required. In the realm of federated learning, model aggregation allows for the combination of knowledge from multiple devices or nodes without the need to share sensitive data, which is crucial for preserving privacy and data security. Thus, model aggregation becomes an essential tool in developing more effective and ethical artificial intelligence solutions.
