The proliferation of transmissions in the RF spectrum demands robust and responsive signal analysis techniques to detect and label malicious activity. Traditional methods struggle to balance sensitivity and accuracy in real-time. This paper introduces Nomad, a modular system that combines global spectral pattern recognition with localized energy detection and classic signal processing methods for efficient and accurate RF analysis. Nomad's innovative architecture facilitates rapid development and deployment, while its intuitive visualizations provide actionable insights into even weak signals within complex RF environments.

An oft-cited open problem of federated learning is the existence of data heterogeneity among clients. One pathway to understanding the drastic accuracy drop in federated learning is by scrutinizing the behavior of the clients’ deep models on data with different levels of "difficulty", which has been left unaddressed. In this paper, we investigate a different and rarely studied dimension of FL: ordered learning. Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the heterogeneity effects in FL. We present theoretical analysis and conduct extensive empirical studies on the efficacy of orderings spanning three kinds of learning: curriculum, anti-curriculum, and random curriculum. We find that curriculum learning largely alleviates non-IIDness. Interestingly, the more disparate the data distributions across clients the more they benefit from ordered learning. We provide analysis explaining this phenomenon, specifically indicating how curriculum training appears to make the objective landscape progressively less convex, suggesting fast converging iterations at the beginning of the training procedure. We derive quantitative results of convergence for both convex and nonconvex objectives by modeling the curriculum training on federated devices as local SGD with locally biased stochastic gradients. Also, inspired by ordered learning, we propose a novel client selection technique that benefits from the real-world disparity in the clients. Our proposed approach to client selection has a synergic effect when applied together with ordered learning in FL.