BAGC-FL: Benign-Anchor Gradient Correction for Imbalance-Aware Federated Intrusion Detection in IoMT Networks

The significant research contribution of this study is the BAGC-FL framework, a server-side Benign-Anchor Gradient Correction mechanism that mitigates the adverse effects of severe class imbalance and label-skewed non-IID data in federated IoMT intrusion detection. Unlike conventional FedAvg and class-weighted training, BAGC-FL selectively removes the gradient component that increases benign-anchor loss while preserving the original model architecture, local BCE objective, and communication protocol. Its effectiveness is demonstrated through a controlled E0–E2 ablation, IID/non-IID evaluation, paired statistical testing, and post-hoc SHAP/LIME analysis.