  The knowledge, embodied in machine learning models for intelligent systems,
is commonly associated with time-consuming and costly processes such as
large-scale data collection, data labelling, network training, and fine-tuning
of models. Sharing and reuse of these elaborated models between intelligent
systems deployed in a different environment, which is known as transfer
learning, would facilitate the adoption of services for the users and
accelerates the uptake of intelligent systems in environments such as smart
building and smart city applications. In this context, the communication and
knowledge exchange between AI-enabled environments depend on a complicated
networks of systems, system of systems, digital assets, and their chain of
dependencies that hardly follows the centralized schema of traditional
information systems. Rather, it requires an adaptive decentralized system
architecture that is empowered by features such as data provenance, workflow
transparency, and validation of process participants. In this research, we
propose a decentralized and adaptive software framework based on blockchain and
knowledge graph technologies that supports the knowledge exchange and
interoperability between IoT-enabled environments, in a transparent and
trustworthy way.
