A Hardware-Oriented Federated GNN Approach for Wireless Localization and Misuser Detection

Wireless edge systems increasingly rely on on-device learning for tasks such as user localization and misuser detection, but centralized training is often infeasible because of privacy and bandwidth constraints. In this work, we study communication-efficient federated learning schemes that jointly train lightweight neural models across clients. We compare a multi-task shared-backbone strategy with mid-training backbone freezing against task-separate baselines. Using a wireless graph simulator, we prototype both multilayer perceptrons (MLPs) and an edge-aware graph neural network (GNN) encoder trained with FedAvg, and we evaluate localization error, misuser F1-score, and byte-level communication over training rounds. The results show that the GNN-Shared model achieves a 28.9% lower localization error than the MLP-Shared baseline and a 17.7% lower localization error than the GNN-Loc_only baseline, while improving the best misuser F1-score by 2.7% compared with GNN-Mis_only. In terms of communication, the GNN-Shared model reduces the total communication cost by 40.1% compared with MLP-Shared over 40 federated rounds. Additional experiments with FedProx, GraphSAGE, and GAT baselines show that the proposed federated graph-learning framework is flexible across different graph backbones. Multi-seed experiments over five random seeds further confirm the robustness of graph-based federated learning, with GraphSAGE-FedAvg achieving strong average localization and misuser detection performance. Scalability experiments with up to 20 clients, 150 nodes, and different Dirichlet non-IID parameters show that the framework remains stable under larger graph sizes, while communication cost grows mainly with the number of participating clients. To move the framework closer to edge deployment, this paper also introduces a lightweight hardware-oriented GNN-Lite inference prototype with multi-neighbor accumulation, finite-state-machine-based sequential computation, and read-only-memory-based coefficient storage. The prototype achieved timing closure on an Artix-7 field-programmable gate array with 213 LUTs, 111 FFs, 18 DSPs, 0 BRAMs, and 0.106 W estimated total on-chip power. The dynamic power was only 0.001 W, and the estimated total energy consumption was 10.6 nJ per inference. Overall, the results show that federated graph learning is a promising direction for wireless edge intelligence, and that different graph backbones can be selected depending on the target trade-off among localization accuracy, misuser detection performance, communication cost, robustness, and hardware deployment.