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Modern vehicles are increasingly equipped with multiple advanced on-board sensors and keep generating large volumes of data. Along with the recent advances in a wide range of Machine Learning (ML) algorithms, the vehicular data are being analyzed intelligently to enable users to be better informed and make safer, more coordinated, and smarter use of transport networks. The success of ML model relies on the availability of large set of relevant data so that the underlying model can be trained better. However, it is not possible for a ML model to fetch the complete set of data from a single vehicle, thus, the collaboration of other vehicles are desired in sharing their local model and collaboratively training the model. Collaborative machine learning (CML) mechanism can improve the intelligence of the ML models in different vehicles by transferring the learned knowledge from the local ML model of one vehicle to another across the distributed network.

In the food industry, there is an increased demand for generic pharmaceutical products and perishable food without compromising with the changes in texture and taste that occur in the transit. With this demand, there is a need for better visibility of products in the logistics network, to minimize wastage, to ensure product integrity, influence productivity, transparently track the fleet and to identify pathogens before a potential outbreak. In Cold Chain Management, information is power: with potentially billions of dollars’ worth of cargo (such as food items, vaccines, serums, tests or chemicals) at stake worldwide. Hence, careful live monitoring, inspection, supervision, validation and documentation of business-critical information is essential.