Private and Secure Machine Learning in Wireless Mobile Communication

As wireless mobile communication continues to evolve, the demand for efficient and accurate Machine Learning (ML) models to manage different use cases has grown sub-stantially. Distributed Collaborative Machine Learning (DCML) techniques offer a promising solution by enabling multiple devices/entities to collaboratively train an ML model without having to share their data with each other. Although these methods can enhance user data privacy, many researches have shown their limitations. One way to ensure privacy in DCML techniques is to use Differential Privacy (DP). DP is a framework that offers mathematically guaranteed privacy. This research paper presents an investigation into the integration of DP mechanisms within DCML frameworks for wireless mobile communication environments. It evaluates the performance of DP and DCML techniques in various aspects of wireless mobile communication, including network traffic analysis, and network slicing. Through experimental simulations, the impact of DP on model performance, convergence rate, and computation overhead is analyzed. The results provide insights into the trade-offs between privacy preservation and ML model effectiveness. This research contributes to the understanding of how the combination of DP and DCML methods can be effectively integrated into wireless mobile communication.