Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence

Abstract

The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language processing, cybersecurity, finance, autonomous systems, and smart environments.Beginning with artificial neural networks and progressing through convolutional and recurrent networks to Transformers, Graph Neural Networks, Vision Transformers, and Large Language Models, deep learning has achieved remarkable improvements in feature representation, prediction accuracy, and knowledge transfer.Nevertheless, the growing complexity of these models introduces several challenges, including limited model transparency, high computational and energy requirements, data privacy risks, and the effective utilization of heterogeneous multimodal information.Unlike conventional surveys that primarily classify deep learning according to network architectures or application domains, this work presents a capability-oriented taxonomy that organizes recent developments into five major research directions: Explainable Deep Learning, Efficient Deep Learning, Privacy-Preserving Deep Learning, Green Deep Learning and Multimodal Deep Learning.Based on this framework, representative architectures are critically examined with respect to their operating principles, strengths, limitations, and suitability for diverse real-world applications.The survey also analyses emerging application areas, identifies unresolved challenges related to fairness, robustness, scalability, and trustworthy artificial intelligence, and discusses promising research opportunities involving Federated Large Language Models, Edge AI, Green AI, Self-supervised Learning, and Multimodal Foundation Models.The proposed framework provides a structured understanding of current advances while offering practical insights for the design of transparent, efficient, secure, and sustainable next-generation deep learning systems.