Myocardial Infarction (MI), commonly referred to as a heart attack, remains a significant challenge in early detection, compounded by the vulnerability of medical data to privacy breaches. This article introduces MI-System, a pioneering framework aimed at facilitating early MI diagnosis through an advanced industrial cyber-physical system (ICPS) coupled with robust privacy-preserving mechanisms. The system is built around a custom-developed Internet of Medical Things (IoMT) device and a state-of-theart security model. MI-System integrates three key layers: 1) the user layer, which gathers photoplethysmography (PPG) signal data via an IoMT device and Smartwatch; 2) the client Layer, where local processing and feature extraction takes place with stringent privacy safeguards; and 3) the cloud layer, which leverages a Federated Learning (FL)based architecture to maintain the integrity of raw data while securing the classification model through differential privacy (DP) and a secure aggregation framework that synchronizes model updates from client devices. To further protect the aggregation process in the FL scheme, an innovative secure, and privacy-focused asynchronous aggregation framework is proposed. In comprehensive realworld trials involving 813 tests across 794 participants, MI-System achieved a remarkably accuracy of high accuracy of 93.51% from the IoMT device, and smartwatches via Android app 94.27%, with minimal latency. This breakthrough enhances MI detection and sets new standards for privacy in smart healthcare, positioning it as a promising candidate for clinical integration worldwide.