To further improve the server-side calibration mechanism for better collaboration between different layers of the model, several enhancements can be considered:
Dynamic Allocation of Resources: Implement a dynamic resource allocation strategy where the server can allocate more resources to specific layers based on their importance or performance. This way, critical layers can receive more attention and updates during the calibration process.
Adaptive Learning Rates: Introduce adaptive learning rates for different layers during server-side calibration. By adjusting the learning rates based on the responsiveness of each layer, the server can ensure that all layers are effectively calibrated without underfitting or overfitting.
Regularization Techniques: Incorporate regularization techniques such as dropout or weight decay during server-side calibration to prevent overfitting and promote generalization across different layers of the model.
Feedback Mechanism: Implement a feedback mechanism where the server can receive feedback from the clients on the performance of different layers. This feedback can be used to adjust the calibration process and focus more on layers that require additional tuning.
Ensemble Methods: Utilize ensemble methods to combine the outputs of different layers during calibration. By aggregating the predictions of individual layers, the server can create a more robust and accurate global model.