CDF-Aware Federated Learning for Low SLA Violations in Beyond 5G Network Slicing

In this paper, we address the concept of dynamic resource allocation for radio access network (RAN) slicing in beyond 5G (B5G) systems under service-level agreement (SLA). Using live network distributed key performance indicators (KPIs) mini-datasets, we introduce a new class of federated learning models that can capture the long-term cumulative distribution function (CDF) statistic—is usually used to define SLA—and enforce some preset constraints on it. Given that the CDF is also dataset-dependent and non-convex non-differentiable, we formulate the corresponding local optimization task using the proxy-Lagrangian framework and solve it via a non-zero sum two-player game strategy. Numerical results show that the proposed decentralized resource allocation approach enables SLA enforcement and significantly reduces the SLA violation rate for various slice-level KPIs.