Demo: Adaptive Resource Allocation Simulator for Federated Learning in MEC-driven SD-WANs

Integrating advanced Machine Learning (ML) techniques, such as Federated Learning (FL), with emerging paradigms like Multi-access Edge Computing (MEC) and Software-Defined Wide Area Network (SD-WAN) has gained significant attention. In this demo, we present a simulator designed to optimize resource allocation for FL in MEC SD-WAN environments, taking into account network capacity constraints. We demonstrate in real-time how resource allocation is implemented for FL algorithms and visually analyze the impact of different constraints on performance. Additionally, we introduce a heuristic algorithm for Distributed Federated Learning (DFL) that selects the optimal global aggregator based on resource utilization. We evaluate the proposed heuristic within our simulation framework and compare its performance against Centralized Federated Learning (CFL) and traditional ML techniques. Our results demonstrate how the proposed simulator effectively integrates network and resource allocation, providing valuable insights into the interplay between FL and MEC SD-WAN infrastructure.