AIoT-Driven Privacy-Preserving and Decentralized Ubiquitous Music Learning

AIoT-driven (Artificial Intelligence of Things) ubiquitous music learning facilitates personalized education anytime and anywhere while raising privacy concerns associated with centralized data processing.To address this, we propose a privacy-preserving and decentralized music learning framework by integrating federated learning and blockchain technologies within the AIoT ecosystem. Learners train models locally on their private data and only share encrypted model updates with decentralized servers, thus mitigating privacy leakage. Furthermore, to defend against poisoning attacks and ensure gradient authenticity and anonymity, a customized ring signature mechanism is employed. Theoretical analysis and experimental results validate the framework’s effectiveness in maintaining privacy and ensuring security.