Transfer Learning in Smart Environments

The knowledge embodied in cognitive models of smart environments, such as machine learning models, is commonly associated with time-consuming and costly processes large-scale data collection, labeling, network training, fine-tuning models. Sharing reuse these elaborated resources between intelligent systems different which known transfer learning, would facilitate the adoption services for users accelerate uptake building city applications. Currently, are built intra-organization purposes tailored towards specific use cases assumption integrated model repositories feature pools. Transferring beyond organization boundaries a challenging task that requires human intervention to find matching evaluate them. This paper investigates potential communication environments order empower decentralized peer-to-peer ecosystem seamless automatic To this end, we explore types context propose collaboration framework based on graph principles describing their corresponding dependencies.