Description: The collective contribution of data from multiple sources in a federated learning scenario refers to an innovative approach where different entities, such as organizations or devices, collaborate to train artificial intelligence models without the need to share their sensitive data. This method allows each participant to contribute their own dataset, enriching the learning process and improving the accuracy of the final model. Through algorithms that enable the aggregation of results, valuable insights can be obtained without compromising the privacy of the information. The joint data contribution is particularly relevant in contexts where data protection is critical, such as in healthcare, finance, or any field that handles personal information. This approach not only fosters collaboration among different actors but also optimizes resource use by avoiding duplication of efforts in data collection and processing. In summary, the collective contribution of data in federated learning represents a significant advancement towards a more ethical and efficient use of artificial intelligence, allowing organizations to benefit from collective intelligence without sacrificing the privacy of individual data.
