Unapologetically Distributed: A Call for Decentralized Document Analysis
This study tested distributed learning for document analysis by training models separately on different datasets and merging them. The research found that decentralization consistently improved model performance, especially in low-resource settings and when dealing with new alphabets or out-of-domain data. This suggests that decentralized training is a valuable strategy for enhancing model robustness in privacy-constrained environments.