ion making, where each entity provides a different view on the same problem. Decision-making needs a way to share the decisions and information coming from different systems. Salman et al. [32] introduced a probabilistic blockchain model to support decision-making data, which can make secure emergency response systems realizable. Use cases for prob- abilistic blockchains still need to be explored and tested. 7.6.2 FEDERATED LEARNING Machine learning models on MEC servers need to be continuously updated to cope with new data and become more personalized to the users it is serving. Traditionally, collected data is sent to the cloud to re-train the machine learning model and then update it on all MEC servers. This technique overloads the cloud and risks the security of data. Khan et al. [33] suggested using federated learning between edge Integrated Blockchain and MEC Systems nodes to overcome centralized model update downsides. In federated learning, only the updated weights are shared, preserving data privacy, and decreasing the amount of data that needs to be shared. Implementing and orchestrating federated learning across MEC servers needs to be further studied. Also, the benefits of large-scale federated learning between MEC servers from different neighborhoods have not been studied. 7.7 CASE STUDY: REAL-TIME PATIENT MONITORING One aspect of Healthcare 4.0 is real-time monitoring, which aims to improve healthcare systems by providing real-time patient data and using AI for diagnosis, analytics, and decision-making [34]. Real-time monitoring includes raising alerts for any severe medical conditions, such as cardiac arrest. Raising early alerts can help patients get aid faster, increasing the survival chances of the patient. AI's diagnosis should also be added to the patient's electronic health records (EHR) automatically. Implementing a real-time monitoring system will increase the number of connected devices, increasing the traffic and the risk of patient data exposure. Furthermore, involved devices and users m