Federated Learning approach for Auto-scaling of Virtual Network Function resource allocation in 5G-and-Beyond Networks

This paper deals with Network Slicing-based 5G Networks to support the varying demands of customers and for efficient resource utilization. A network slice can be defined as a set of network and virtual network function (VNF) resources deployed across multiple administrative domains. Here, multi-domain refers to multiple infrastructure providers spread across different geographic regions. Slice demands and QoS requirements may vary dynamically, which can be satisfied by scaling the allotted VNF resources. The VNF scaling problem can be posed as a time series forecasting problem that predicts future VNF resources based on the slice traffic demand. 5G deployments with multiple domains pose a serious challenge in terms of data privacy since one domain may need access to the data of another domain for efficient resource allocation using the conventional forecasting approaches that requires data aggregation. In this paper, we use the federated learning approach in which the training data remains within the respective domains but learns a shared model by aggregating locally-computed updates. We evaluate the applicability of federated settings in VNF scaling using two state-of-the-art deep learning models, Long Short-Term Memory (LSTM) and Gated recurrent units (GRU). We present a comparison of the performance of the proposed federated system against the centralized system. Additionally, synthetic data in each domain has been generated using Generative Adversarial Networks (GAN) to improve the forecasting results.