Abstract
Advances in Deep Learning have enabled applications in critical domains, yet challenges remain regarding model explainability and data quality, particularly in distributed learning settings such as the travelling model paradigm. In these scenarios, decentralization and data confidentiality hinder direct control over training data, limiting process interpretability. This project proposes a…