FedMAD-FSL: Advanced Few-Shot Federated Meta-Learning Framework for Anomaly Detection in Heterogeneous IoT Ecosystems with Adaptive Privacy-Preserving Mechanisms

Abstract

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in anomaly detection, particularly in scenarios with limited labeled data and strict privacy constraints. This paper introduces FedMAD-FSL (Federated Meta-learning for Anomaly Detection with Few-Shot Learning), an integrated framework that synergistically combines federated learning, metalearning, and few-shot learning paradigms to enable rapid few-shot anomaly detection across heterogeneous IoT networks. Our approach features a novel hierarchical meta-learning architecture with multi-scale cross-attention mechanisms, adaptive prototype networks with dynamic memory management and task-conditioned hallucination, a spectral-domain gradient compression algorithm with formal differential privacy guarantees, and a novel contrastive meta-regularization objective for improved generalization. The framework incorporates advanced few-shot learning techniques including prototypical networks enhanced with von Mises-Fisher distribution modeling, matching networks augmented with set-to-set transformers, and relation networks with graph neural networkbased relational reasoning, all unified under an adaptive meta-curriculum learning scheduler and adversarial robustness mechanisms. Comprehensive experiments on twelve real-world IoT datasets demonstrate that FedMAD-FSL achieves 97.8% weighted-average detection accuracy with only 1-5 labeled samples per device type, 84.8% reduction in total communication cost (23.7MB vs. 156.3MB for FedAvg), achieved through spectral-domain gradient compression with an 89.3% compression ratio, and 94% faster convergence compared to state-of-the-art methods. The system successfully adapts to completely unseen IoT environments with 95.3% accuracy using only 1-5 labeled samples while maintaining (ε = 0.3, δ = 1e−6)-differential privacy, establishing few-shot federated meta-learning as a transformative paradigm for next-generation IoT security.