Designing Resilient Deep Learning Models for Intelligent Infrastructure: Confronting Scalability, Security, and Privacy Challenges

Abstract

The integration of deep learning models into intelligent infrastructure systems presents significant opportunities for enhancing efficiency, safety, and resilience in urban environments. However, the development and deployment of these models come with critical challenges related to scalability, security, and privacy. This paper provides a comprehensive examination of these challenges and proposes solutions for developing robust deep learning models for intelligent infrastructure. We analyze the technical requirements for scaling deep learning models across large infrastructure networks, addressing the computational and data management needs. Additionally, we explore security vulnerabilities inherent in deep learning models, such as adversarial attacks and data poisoning, and discuss methods for mitigating these risks. Privacy concerns arising from the collection and use of sensitive data are also addressed, with an emphasis on techniques such as federated learning and differential privacy to protect user information. By tackling these issues, we aim to provide a framework for the safe, efficient, and scalable deployment of deep learning models in intelligent infrastructure systems.