Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing

Hatim Chergui

doi:10.1109/TWC.2021.3109377

Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing

2022-03-01

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DOI：10.1109/TWC.2021.3109377

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A key enabler for both scalability and sustainability in beyond 5G (B5G) network slicing consists on minimizing the exchange of raw monitoring data across different domains. This is achieved by bringing the analysis functions closer to the data collection points. To this end, we introduce in this paper statistical federated learning (SFL) provisioning models that can learn over a live network non independent identically distributed (non-IID) datasets in an offline fashion while respecting slice-level service level agreement (SLA) long-term statistical constraints. Specifically, we consider three resource SLA metrics, namely, cumulative distribution function (CDF), Q-th percentile and maximum/minimum bounds. These metrics are dataset-dependent and non-convex non-differentiable and, to sidestep the inaccuracy of settling only for surrogates, we propose a novel formulation that jointly considers the statistical objective and constraints as well as their smooth approximation using the proxy-Lagrangian framework, which we solve via a non-zero sum two-player game strategy. Numerical results on various slice-level resources show that SFL enables SLA enforcement while significantly reducing the overhead compared to both state-of-the-art FedAvg and centralized constrained deep learning schemes. Finally, we provide an analysis for the lower bound of the so-called reliable convergence probability in the SFL setup.

Keywords:

B5G

cloud-RAN

dynamic slicing

game theory

proxy-Lagrangian

resource allocation

SLA

statistical federated learning

violation rate

Statistical Federated Learning for Beyond 5G SLA-Constrained RAN Slicing

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