Differentially Private Federated Multi-Task Learning Solution for HDT

This chapter introduces an innovative approach designed to establish a robust, secure, and efficient connection between users and their virtual counterparts in HDT systems. This connection scheme integrates three fundamental techniques: differential privacy, federated multi-task learning, and blockchain technology, collectively ensuring a comprehensive and privacy-preserving solution. To delve further into the details, federated multi-task learning is a pivotal component of this approach. By employing this technique, the system can effectively account for the diverse and often disparate environments in which human users and virtual twins may operate. This adaptability is essential in enhancing the overall performance and relevance of the virtual twin. Furthermore, the chapter introduces an innovative validation process centred around the quality of the models developed during the federated multi-task learning phase. This process plays a crucial role in ensuring that the evolution of the virtual twin models remains accurate and authorized. This, in turn, guarantees that the virtual twin continues to reflect the real-world user accurately while operating in the virtual environment.