Deep Learning for Edge Computing Applications: A Comprehensive Survey


  • Mohamed Buhary Fathima Sanjeetha Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  • Yasanthy Kanagaraj Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  • Vihangi Herath Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  • Shashika Lokuliyana Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka



Security, IoT, Federated Learning, Edge Server, Edge Computing


Edge computing is a modern computer architecture that processes data quickly and efficiently close to its point of origin, hence avoiding slowdowns caused by network latency and capacity limitations. By moving processing power to the network’s perimeter, edge computing decreases the load on central data centers and reduces the time it takes for users to submit data. Therefore, access latency may become a barrier, potentially negating the benefits of edge computing, particularly for applications that need a great deal of data.. Edge computing has some challenges, such as security, incomplete data, investment costs, and maintenance costs. In this research, we undertake a thorough analysis of edge computing, how edge device placement improves performance in IoT networks, compare various edge computing implementations, and explain various difficulties encountered during edge computing implementation. This study aims to promote creative edge-based Internet of Things security design by thoroughly examining existing Internet of Things security solutions at the edge layer and facilitate the dynamic deployment of edge devices.


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How to Cite

Fathima Sanjeetha, M. B., Kanagaraj, Y., Herath, V., & Lokuliyana, S. . (2022). Deep Learning for Edge Computing Applications: A Comprehensive Survey. Asian Journal of Computer Science and Technology, 11(2), 39–47.