A Novel Framework of Federated and Distributed Machine Learning for Resource Provisioning in 5G and Beyond using Mobile-Edge SCBS

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IEEE Transactions on Network and Service Management


The future needs of the telecommunication system lie in deploying a heterogeneous ultra-dense network with varied topographical use cases. However, this increase in ultra-denseness in 5g and beyond poses several challenges in resource allocation, requiring an accurate learning-based prediction. This paper proposes a novel framework using Federated Learning (FL) and Distributed Machine Learning (DML) for Mobile Edge based resource provisioning to User Equipment (UEs). This work formulates the correlation-based novel procedures between UEs in applying Federated and Distributed Machine Learning through Kolmogorov tests for predicting SNR. The correlations of the distribution obtained through the Kolmogorov test check the extent of Independent and Identically Distributed (IID) -ness between modelled data and evaluate the global model for resource provisioning accuracy. Further, correlation-based DML is also employed to balance the computational load of a mobile edge, which acts as a small cell base station and a computational node. In this approach, we account for correlation-based resource predictive model training to balance the uniform computational load by data distribution methods among the neighbouring mobile Edge SCBS nodes for computation. Together with both DML and FL, we create a novel Framework for resource prediction with minimal time for achieving high accuracy without over-fitting.

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5G mobile communication systems, Base stations, Forecasting, Machine learning, Mobile edge computing, Radio access networks, Resource allocation


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