FedImpro's approach can be applied to other machine learning models by incorporating the concept of decoupling neural networks and constructing similar feature distributions. This method can help reduce gradient dissimilarity in federated learning settings, leading to improved generalization performance. By splitting the model into high-level and low-level components and training on reconstructed feature distributions, other machine learning models can also benefit from reduced gradient dissimilarity and enhanced generalization contribution. The use of Gaussian distribution approximation for feature estimation can make this approach computationally efficient and privacy-preserving.