Machine Learning for Edge Computing and Synthetic Data Generation in cloud computing
Monish Kumar A
, Harish V , Akash P , Raajhalingam T
Machine Learning, Edge Computing, Synthetic Data Generation, Cloud Computing
Machine learning (ML) has become an increasingly important tool for analysing and processing large amounts of data in various domains. However, traditional cloud-based ML approaches suffer from latency and bandwidth issues, which makes them unsuitable for certain applications such as real-time analytics and Internet of Things (IOT) devices. Edge computing has emerged as a promising solution to this problem by enabling ML algorithms to be executed on edge devices, which are closer to the source of the data. This state-of-the-art survey provides a comprehensive review of the latest research on machine learning for Edge Computing in cloud computing. It covers various aspects of the field, including designing efficient ML algorithms for edge devices, the challenges and opportunities in deploying ML models in Edge Computing environments, and the integration of Edge Computing with cloud-based infrastructures. The survey also discusses the key issues related to security and privacy in Edge Computing, including the protection of sensitive data and the prevention of cyber-attacks. Additionally, it examines the role of edge devices in enabling new types of applications, such as autonomous vehicles and smart cities, and explores the potential benefits and drawbacks of Edge Computing for ML. Overall, this survey provides a comprehensive overview of the latest research on machine learning for Edge Computing in Cloud Computing, highlighting the challenges and opportunities in this rapidly evolving field. The availability of large and numerous datasets is a crucial problem in the success of device-studying algorithms. However, collecting and labelling such datasets can be time-consuming and expensive. Synthetic data generation techniques offer a promising solution to this problem by creating realistic synthetic data that can be used to train and validate machine learning models. We also discuss the potential benefits and drawbacks of synthetic data and its applications in cloud computing, such as cyber security, healthcare, and financial fraud detection. Finally, we highlight the challenges and opportunities in synthetic data generation, such as scalability, quality control, and ethical considerations.
"Machine Learning for Edge Computing and Synthetic Data Generation in cloud computing", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.685-690, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001108.pdf
Volume 11
Issue 2,
February-2024
Pages : 685-690
Paper Reg. ID: TIJER_151024
Published Paper Id: TIJERB001108
Downloads: 000265
Research Area: Science and Technology
Country: Thiruvallur,Chennai, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
