Towards Trustworthy Sign Language Translation System: A Privacy-Preserving Edge–Cloud–Blockchain Approach
Nada Shahin, Leila Cheikh Ismail · Mathematics · 2025
The growing Deaf and Hard-of-Hearing community faces communication challenges due to a global shortage of certified sign language interpreters. Therefore, developing efficient and secure sign language machine translation (SLMT) systems is essential. Current work addresses the accuracy of the sign language translation task. However, there is a need for an SLMT system that encompasses privacy, efficiency, translation accuracy, and Machine Learning development operations. This paper addresses this void by proposing a novel consent-aware privacy-preserving end-to-end edge, cloud, and blockchain integrated computing system. We evaluate the system by comparing the mostly used Encoder–Decoder Transformer and a lightweight Adaptive Transformer (ADAT), using two datasets: the most comprehensive sign language dataset RWTH-PHOENIX-Weather-2014T (PHOENIX14T), and MedASL, our newly developed medical-domain dataset. A comparative analysis of translation quality on PHOENIX14T shows that ADAT improves BLEU-4 by 0.02 absolute points and ROUGE-L by 0.11. On MedASL, ADAT gains 0.01 in BLEU-4 and 0.02 in ROUGE-L. For runtime efficiency on MedASL, ADAT reduces training time by 50% and lowers both edge–cloud and end-to-end system communication times by 2%.