Abstract: To enable the large scale and efficient deployment of Artificial Intelligence (AI), the confluence of AI and Edge Computing has given rise to Edge Intelligence, which leverages on the computation and communication capabilities of end devices and edge servers to process data closer to where it is produced. One of the enabling technologies of Edge Intelligence is the privacy preserving machine learning paradigm known as Federated Learning (FL), which enables data owners to conduct model training without having to transmit their raw data to third-party servers. However, the FL network is envisioned to involve thousands of heterogeneous distributed devices. As a result, communication inefficiency remains a key bottleneck
Keywords: cloud, network, infrastructure, data security
Works Cited:
Harish Babu P, Sundar Rajan, Kumaran. M " A DYNAMIC RESOURCE ALLOCATION FOR HIERARCHICAL FEDERATED LEARNING USING DECENTRALIZED EDGE INTELLIGENCE ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 9, pp. 109-113, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.12919
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DOI:
10.17148/IJARCCE.2023.12919
