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• W4308946221 abstract "Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed. • A novel on-demand client deployment in Federated learning. • Targeting the problem of clients availability in pre-configured FL areas. • Targeting clients that do not have the needed capabilities for learning. • An efficient orchestration and deployment of ML services on newly formed client devices." @default.
• W4308946221 created "2022-11-19" @default.
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• W4308946221 date "2023-01-01" @default.
• W4308946221 modified "2023-10-11" @default.
• W4308946221 title "On the feasibility of Federated Learning towards on-demand client deployment at the edge" @default.
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• W4308946221 doi "https://doi.org/10.1016/j.ipm.2022.103150" @default.
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