Abstract
Big data and cloud computing lay a new paradigm of data analytics and pave an insight for new types of intelligent devices. These smart devices continuously capture, store, and transfer data to the centrally controlled devices. In recent years, there has been an exponential increase in smart devices, which has increased the amount of data generated by these devices. The communication of the generated data to the central nodes results in a high communication rate. This type of data communication is vulnerable to network intrusions and multiple security threats. Realtime processing of the data is also a huge challenge, and it is an arduous task to provide support to the applications working on realtime decision making. This paper proposes a message passing approach among intelligent edge devices, which leverages the advantages of the big data analytics and federated learning to tackle the challenges as mentioned above. The experimental analysis of several benchmark datasets supports the proposed approach’s theoretical base. The proposed approach with suitable number of edge devices reduces the overall training time maximum by one third while maintaining a significant amount of accuracy.
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Chowdhury, A., Swaminathan, A., Ashoka, R.R., Pal, A. (2023). Enabling Edge Devices Using Federated Learning and Big Data for Proactive Decisions. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_24
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