Description: The evaluation of the joint model in the context of federated learning refers to the process of measuring the performance of a model that has been collaboratively trained across multiple devices or servers, without the need to centralize data. This approach allows models to benefit from the diversity of distributed data, enhancing their generalization capability and reducing the risk of overfitting. Evaluation is conducted using specific metrics that reflect the model’s effectiveness in tasks such as classification, regression, or anomaly detection. Unlike traditional methods, where data is collected and processed in a single location, federated learning allows data to remain in its original location, improving privacy and security. During evaluation, distributed validation datasets can also be used, enabling a more realistic assessment of the model’s performance in various conditions. This process is crucial to ensure that the model not only performs well on training data but is also effective in practical situations where it may encounter unseen data. In summary, the evaluation of the joint model is an essential component of federated learning, ensuring that models are robust, accurate, and applicable in diverse environments.
