Description: Joint Evaluation is a fundamental process in the field of federated learning, referring to the assessment of a machine learning model’s performance using data from multiple sources. Unlike traditional approaches, where data is centralized on a single server, federated learning allows models to be trained locally on distributed devices or servers, preserving data privacy. Joint Evaluation is conducted at the end of a training cycle, where performance metrics from models trained on each data source are collected. This process not only ensures that the generalized model is robust and effective but also helps identify potential biases or deficiencies in the learning. The main characteristics of Joint Evaluation include the ability to handle heterogeneous data, privacy preservation, and continuous model improvement through feedback from multiple sources. Its relevance lies in the growing need for artificial intelligence solutions that respect data privacy and security, especially in sectors like healthcare, finance, and telecommunications, where sensitive information is common. In summary, Joint Evaluation is an essential component that ensures the effectiveness and ethics in the development of federated learning models.
