Prospects of Cloud-Driven Deep Learning- Leading the Way for Safe and Secure AI
Jay Patel · INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND APPLIED SCIENCES · 2020
The combination of cloud computing and deep learning models is changing the field of artificial intelligence (AI), facilitating more scalable, efficient, and adaptable systems for intricate data-driven activities. Nonetheless, as AI systems grow more powerful and widespread, guaranteeing their safety and security continues to be a significant challenge. This document investigates the future of cloud-based deep learning, specifically emphasizing innovative approaches to guarantee AI safety and security. We explore the capabilities of cloud-based systems in facilitating extensive deep learning models, highlighting their benefits in resource management, model training, and instantaneous inference. The article further explores the new risks linked to cloud-based AI, including adversarial threats, data privacy issues, and challenges with model robustness. To tackle these challenges, we suggest a framework that incorporates security measures such as secure multi-party computation, federated learning, and differential privacy into cloud-based deep learning workflows. Additionally, we examine the function of explainable AI (XAI) in improving trust and responsibility within cloud-based AI systems. By conducting an extensive analysis of recent progress, this paper offers perspectives on the future trajectory of safe and secure AI in the cloud, highlighting the significance of strong security frameworks, transparency, and ethical factors in AI implementation. As cloud infrastructures advance, the incorporation of sophisticated security functionalities will be crucial for the ethical development and implementation of deep learning technologies.