Safe Offloading Based on Federated Learning in the Fog Computing Environment Using Software-Defined Networks
Subject Areas : electrical and computer engineeringM. R. Sharafi Hoyavada 1 , Mohammad Reza Mollahosseini 2 * , Vahid Ayatollahitafti 3
1 - Dept. of Comp. Eng., Meybod Branch, Islamic Azad University, Meybod, Iran
2 - Dept. of Comp. Eng., Meybod Branch, Islamic Azad University, Meybod, Iran
3 - Department of Computer, Taft Branch, Islamic Azad University, Taft, Iran
Keywords: Software-defined network, federated learning, edge computing, Internet of Things.,
Abstract :
The Internet of Things poses significant challenges in data processing and storage due to the large volume of data generated, including latency, location awareness, and real-time mobility support. Edge computing is recognized as an effective solution to these challenges. This paper examines various secure offloading methods based on collaborative learning in edge computing environments using software-defined networking and analyzes four optimization methods: SDN, SA+GA, OLB-LBMM, and Round-Robin. The main objective of this research is to improve performance and security in the data offloading process while addressing existing challenges. The SDN method provides a flexible framework for managing resources and data in IoT networks, demonstrating better performance than other methods. By reducing latency and optimizing resource allocation, it enhances user satisfaction and increases revenue for cloud service providers. Additionally, the SA+GA and OLB-LBMM algorithms offer improvements in efficiency and security, although they face challenges related to latency and computational complexity. The results indicate that collaborative learning combined with SDN can significantly enhance secure data offloading and enable dynamic network resource management. This research can serve as a foundation for future studies aimed at optimizing data offloading processes in edge computing environments.
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