(untitled) (Computer systems and information technologies, 2025)
Along with the rapid growth of the Internet of Things and edge computing, collaborative training of machine learning models on resource-constrained edge devices, while ensuring user data privacy, has become a key and challenging research challenge. The presented paper aims to fill this research gap by designing, implementing, and evaluating a synergistic and optimized federated learning infrastructure with differential privacy called PriFed-IoT, specifically designed for Internet of Things edge computing scenarios. The novelty of this work lies in the creation of a system of several modules working together, rather than a simple combination of techniques. The main idea is to use adaptive differential privacy to create an environment with a higher signal-to-noise ratio for the clustering algorithm in the late stages of training, allowing for more accurate separation of clients. In order to test the efficiency of the PriFed-IoT infrastructure, a series of complex simulation experiments were developed and conducted on a standard CIFAR-10 dataset, simulating different degrees of data heterogeneity. The study successfully offers a comprehensive solution, the experimental results convincingly prove the advantages of PriFed-IoT infrastructure in balancing privacy protection, model utility, and system efficiency, providing a valuable theoretical framework and technical implementation for building secure, efficient, and reliable intelligent applications at the edge of the Internet of Things.