In order to extend the federated learning approach to handle more complex multi-class ransomware detection scenarios, several adjustments can be made. Firstly, the model architecture can be modified to accommodate multiple classes by incorporating additional output nodes in the final classification layer corresponding to each class. This would require redefining the loss function to encompass all classes and updating the aggregation process to consider the gradients from each client for all classes. Additionally, the communication strategy between clients and the central server may need to be enhanced to ensure efficient sharing of model updates and weights for multi-class classification. Moreover, techniques such as ensemble learning can be employed to combine the knowledge from multiple models trained on different subsets of classes, thereby improving the overall detection performance in a federated learning setting.