Sukanya Mandal

Recent advancements in AIoT (Artificial Intelligence + Internet of Things) techniques have made possible continuous advancements of various smart city applications in various sectors. Cognitive Digital Twin (CDT) – a Knowledge Graph and AI based virtual replica of the physical world, has been adopted by various industries especially the manufacturing sector but has been significantly slow adoption for smart city. The major reason being – lack of trust and privacy concerns towards sharing sensitive data. Privacy Preserving Federated Learning (PPFL), could be integrated along with CDT to ensure privacy preservation and trustworthiness. This research proposes a framework for integrating PPFL and CDT technologies to address various real life smart city scenarios as well as enable feasibility for smart city governance.

A CDT is an extended or augmented version of DT with cognitive capabilities. It contains at least the three basic elements of DT – the physical entity (systems, subsystems, components etc.), digital (or virtual) representation or shadows and the connections between the virtual and physical spaces. The main difference is that CDT usually contains multiple DT models with unified semantics topology definitions. In addition, a CDT should – have cognition capabilities, have a digital version of the entire lifecycle of a system, be autonomous and be able to continuously evolve with the physical system across the entire lifecycle. The core idea of Federated Learning (FL) is to train machine learning models on datasets that are distributed across different devices or parties, which can preserve the local data privacy. Privacy Preserving Federated Learning (PPFL) focuses on the privacy preserving mechanism of FL by employing privacy preserving techniques such as Homomorphic Encryption (HE), Secure Multi-party Computation (SMC) and Differential Privacy (DP).

The motivation behind coupling PPFL and CDT are quite a few. Even though CDT has its own benefits, it lacks maintaining data privacy as data has to be transferred to the CDT from various sensors and physical entities. In PPFL, the privacy is preserved at the end user as only weights and parameters updates are transmitted from the edge devices and not the data. Hence raw sensitive information stays with the device and is not communicated over the network. The communications between smart city DTs as well as between DTs and edge devices is privacy-preserved. Last but not least, data quality and integrity is improved as pre-processing is done at the edge environment.

Research Objectives and Directions:

• Create Ontology and Knowledge Engineering models for smart cities (serving as the data layer).
• Research on creating a GNN based framework (or other enabling technology) to form the cognition capability of  the CDT framework basis graph data models catering to smart cities.
• Research on a framework creation of Privacy Preserving FL in general and specialise it for adoption of CDT.