Adaptive Federated Edge Transformer with Multi-Level Knowledge Distillation and Hybrid GA–SQP Optimization for Privacy-Preserving Zero-Day Attack Detection in Heterogeneous IoT Network

The increasing availability of diverse internet of things devices has made possible an increase in zero-day attacks, but at the same time, it has brought up several issues related to data privacy, overhead communications, and computation scalability for central intrusion detection systems. Federated learning provides an effective decentralized approach for cooperative cyber threat intelligence; however, its efficiency is hindered by non-independent and identically distributed data, unstable client contribution, restricted resources of edge devices, and ineffective model aggregation. In order to overcome the aforementioned problems, this paper presents an Adaptive Trust-Aware Federated Edge Transformer (AFET) with multi-level knowledge distillation (MLKD) and hybrid Genetic Algorithm–Sequential Quadratic Programming (GA–SQP) optimization for privacy-preserving zero-day attack detection in diverse IoT environments. The AFET architecture implements an adaptive trust-aware model aggregation process that adjusts the aggregation weights based on the clients’ reliability and data quality, whereas the MLKD module implements knowledge transfer from a global teacher network to edge transformers through multi-level information sharing. In addition, the hybrid GA–SQP optimizer optimizes the parameters of both the transformer and federated learning algorithms. The experimental results performed on benchmark intrusion detection datasets prove that the proposed framework attains detection accuracy of 99.18%, precision of 98.07%, recall of 97.91%, F1-score of 98.03%, and AUC of 0.998. This is an alternative tool for privacy-preserving zero-day attack detection in heterogeneous IoT networks.