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#federated learning Open access Sep 2026

Attribution Drift in Federated Intrusion Detection — code and artifacts

Federated learning (FL) trains a shared network-intrusion detector across organisations without pooling raw traffic, and such deployments increasingly demand explainability. Yet FL traffic is deeply **non-IID**, and heterogeneity's effect on a model's *explanations* is uncharacterised. We audit it. Sweeping the Dirichl...

Anonymous · 0 citations
#federated learning Open access Sep 2026

Attribution Drift in Federated Intrusion Detection — code and artifacts

Federated learning (FL) trains a shared network-intrusion detector across organisations without pooling raw traffic, and such deployments increasingly demand explainability. Yet FL traffic is deeply **non-IID**, and heterogeneity's effect on a model's *explanations* is uncharacterised. We audit it. Sweeping the Dirichl...

Anonymous · 0 citations

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