Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection

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arXiv:2610.19808v1 Announce Type: new Abstract: Network Intrusion Detection Systems (NIDS) based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated learning (FL) settings where data heterogeneity and client drift pose additional challenges. Prior work (TabFIDS) showed that FedAvg combined with bootstra