A Federated Learning-Based Virtual Interpreter for Arabic Sign Language Recognition in Smart Cities

Arabic Sign Language (ASL) exhibits structured grammar and syntax, necessitating adherence to these rules in automated sign language generation for people with disabilities. Hence, the effective generation of sign language relies heavily on using 3D virtual signers. This paper presents a groundbreaking framework that employs federated learning to develop a virtual interpreter for ASL, aimed at enhancing accessibility and communication for the deaf community in smart cities. The proposed paradigm establishes a baseline to facilitate the creation of innovative applications that generate contextually and grammatically accurate sign language interpretations. By employing federated deep learning, the framework maintains user privacy while allowing for continuous improvement of the interpreter's performance. This paradigm aims to promote inclusivity and assistive technologies by integrating sign language into urban technological solutions.