Multi-Agent Agentic AI and Federated Learning for Attack Detection in Sustainable IoT

The Internet of Things (IoT) comprises a vast number of digitally networked devices. Machine learning (ML), deep learning (DL), and federated learning (FL), all subfields of artificial intelligence (AI), have recently been investigated as cybersecurity solutions for protecting these networked devices from intrusions and cyberattacks. However, existing ML and DL techniques for Sustainable IoT data carry significant privacy implications. Because of its inherent suitability for IoT settings, AI-based cybersecurity, and FL in particular, has become critical, yet traditional FL-based approaches themselves fall short in several respects. We need a framework that prioritizes privacy while enabling effective FL on edge-generated data. This research aims to address three persistent problems in traditional FL-based systems: class imbalance, unequal data distribution across devices, and computational complexity. To this end, we propose a novel Multi-Agentic AI framework in which each device decomposes its local workload across three cooperating: Agent 2 performs data preprocessing, including WGAN-based oversampling to correct class imbalance; Agent 1 performs data distribution, applying a vertical federated learning (VFL) partitioning scheme to address the unequal data distribution that typically affects FL-based IoT systems; and Agent 3 performs classification, training a deep autoencoder and an MLP on the resulting features while keeping the computational load on each device manageable. Experiments on the Modbus ICS network and ToN-IoT datasets show that our framework improves on the performance of comparable ML models while overcoming the limitations of existing FL-based techniques. Our VFL approach delivers consistent data distribution and higher attack detection accuracy, reaching 99.56% and 99.62% on the two datasets, respectively. Overall, this multi-agent agentic AI methodology offers a practical way to address the core challenges of FL and meaningfully improve AI effectiveness in privacy-sensitive sustainable IoT domains.