In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.

最近，机器学习 (ML) 社区已将深度学习 (DL) 计算模型视为黄金标准。深度学习逐渐成为机器学习领域应用最广泛的计算方法，在各种复杂的认知任务中取得了媲美甚至超越人类表现的显著成果。DL 的主要优势之一是它能够从大量数据中学习。近年来，深度学习领域发展迅速，并在各种常规领域得到成功应用。值得注意的是，深度学习在云计算、机器人、网络安全等多个领域的表现都优于成熟的机器学习技术。如今，由于物联网网络的不断发展，云计算已变得至关重要。它仍然是将复杂的计算应用程序投入使用、强调大量数据处理的最佳方法。然而，由于尖端物联网应用程序的关键局限性产生大量数据并且需要快速反应时间并增加隐私，因此云计算不足。最新的趋势是采用去中心化的分布式架构，将处理和存储资源转移到网络边缘。这消除了云计算的瓶颈，因为它使数据处理和分析更接近消费者。机器学习 (ML) 在网络边缘越来越多地被用于增强计算机程序，特别是通过减少延迟和能源消耗，同时增强资源管理和安全性。为了在效率、空间、可靠性和安全性以及最小的功耗，需要深入研究以开发和应用机器学习算法。这种对流行计算范式的全面检查强调了机器学习和新兴计算模型的集成所带来的最新进展，同时还解决了潜在的开放研究问题以及潜在的未来方向。因为它被认为为跨学科研究和商业应用开辟了新的机会，我们在这篇文章中对涉及深度学习与各种计算范式（包括云、雾、边缘和物联网）融合的最新工作进行了全面评估。贡献。我们还提请注意主要问题和未来可能的研究方向。