Hierarchical Federated Learning for Unsupervised Waveform Classification over Tactical MANETs

Abstract

Distributed radio frequency sensing in contested tactical environments demands collaborative learning across mobile nodes. In ad-hoc networks, learning must occur without persistent backhaul, ground truth labels, or reliable communication links. Traditional federated learning approaches assume either ideal link conditions or supervised training objectives, neither of which holds in practice for deployed MANET platforms. This paper presents a hierarchical federated learning framework for unsupervised waveform classification over tactical MANETs subject to Rayleigh fading, random waypoint mobility, and multi-hop routing loss. Each node trains a local denoising convolutional autoencoder on raw IQ observations without label exchange, learning compact representations through a self-supervised reconstruction objective. A two-stage aggregation protocol elects connectivity-based relay aggregators consistent with OLSR multipoint relay selection, compressing cluster-level model updates before forwarding to a mobile server proxy. Across five random seeds, in-network aggregation reduces attempted transmission bits by 16% on average relative to relay-forward federated averaging at comparable classification performance. Despite mean per-round update drop rates of 21% (hierarchical) and 34% (flat), hierarchical MANET FL attains the highest mean unsupervised representation quality of the three federated conditions tested while also showing substantially lower run-to-run variance than either flat MANET FedAvg or ideal FedAvg; flat routing itself shows no consistent benefit or penalty relative to ideal FedAvg. Performance is assessed using KMeans normalized mutual information and linear probe accuracy on the learned latent embeddings.