FedMAD-FSL: Advanced Few-Shot Federated Meta-Learning Framework for 1 Anomaly Detection in Heterogeneous IoT Ecosystems with Adaptive Privacy- 2 Preserving Mechanisms

Abstract

The proliferation of Internet of Things (IoT) devices has created unprecedented 10
challenges in anomaly detection, particularly in scenarios with limited labeled data and strict 11
privacy constraints. This paper introduces FedMAD-FSL (Federated Meta-learning for Anomaly 12
Detection with Few-Shot Learning), an integrated framework that synergistically combines 13
federated learning, meta-learning, and few-shot learning paradigms to enable rapid few-shot 14
anomaly detection across heterogeneous IoT networks. Our approach features a novel 15
hierarchical meta-learning architecture with multi-scale cross-attention mechanisms, adaptive 16
prototype networks with dynamic memory management and task-conditioned hallucination, a 17
spectral-domain gradient compression algorithm with formal differential privacy guarantees, and 18
a novel contrastive meta-regularization objective for improved generalization. The framework 19
incorporates advanced few-shot learning techniques including prototypical networks enhanced 20
with von Mises-Fisher distribution modeling, matching networks augmented with set-to-set 21
transformers, and relation networks with graph neural network-based relational reasoning, all 22
unified under an adaptive meta-curriculum learning scheduler and adversarial robustness 23
mechanisms. Comprehensive experiments on twelve real-world IoT datasets demonstrate that 24
FedMAD-FSL achieves 97.8% weighted-average detection accuracy with only 1-5 labeled 25
samples per device type, 84.8% reduction in total communication cost (23.7 MB vs. 156.3 MB 26
for FedAvg), achieved through spectral-domain gradient compression with an 89.3% 27
compression ratio, and 94% faster convergence compared to state-of-the-art methods. The 28
system successfully adapts to completely unseen IoT environments with 95.3% accuracy using 29
only 1-5 labeled samples while maintaining (ε = 0.3, δ = 1e-6)-differential privacy, establishing 30
few-shot federated meta-learning as a transformative paradigm for next-generation IoT security. 31
Keywords: federated learning; meta-learning; few-shot learning; anomaly detection; IoT 32
security; differential privacy; prototype networks; communication efficiency