FedTransGuard: Privacy-Centric Smart Package Surveillance Using IoT and Federated Learning

Abstract

In order to safeguard sensitive documents and valuable assets while in transit this paper suggests an intelligent and secure package transportation system that combines Federated Learning (FL) and Internet of Things (IoT) technologies. The system is designed to detect unusual activity prevent package tampering and enforce adherence to a predetermined transportation route between source and destination locations. A GPS tracker for continuous real-time route surveillance a fingerprint sensor for biometric access control a tamper detection sensor for monitoring physical interference and a Raspberry Pi for onboard processing are all included in each package unit. Individual smart boxes operate as slave nodes under a federated learning-based master-slave architecture, processing sensor data locally and sending only compact status signals—such as tamper alerts anomaly detection, normal operation or unauthorized access events—to a designated master node without disclosing sensitive raw sensor data. The master node securely transmits a condensed fleet health report to the central server aggregates received statuses, and conducts an independent self-diagnostic on its own package. The server then sends out real-time alerts and notifications to authorized staff about the package's security and transportation status. The suggested framework offers an effective real-time monitoring solution for secure asset delivery in logistics operations greatly improving transportation security, protecting data privacy and lessens exposure to centralized data.