Collaborative computing systems used today are a significant problem, and the variety of attacks and data privacy protection play an important role. Accordingly, this research takes the latest technology, such as federated learning and cloud-edge collaborative computing systems. Among them, multi-national validation with attacks/without attacks architecture is mainly developed, and 'End-to-end privacy-preserving deep learning for attack classification method is used to classify each episode that occurs and done through End-to-end privacy-preserving deep understanding (E2EPPDL). This is the most essential core component of our research. We justified them with Time, Node Count, Routing count, and Data delivery ratio estimates.

Attacks classification and data privacy protection in cloud-edge collaborative computing systems