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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint studies the interaction between differential privacy, Byzantine-robust aggregation, and minority-class detection in federated intrusion detection using a single public dataset (UNSW-NB15) and DP-SGD with coordinate-wise median aggregation. The work provides initial empirical evidence that privacy noise and robust aggregation jointly degrade detection of rare attacks disproportionately, but is exploratory and unvalidated across datasets or operational settings.
Empirical case study using a single public dataset. Network intrusion detection scenarios with severely imbalanced attack categories; evaluation using UNSW-NB15 dataset.. Intervention: Federated learning with differential privacy (DP-SGD) and Byzantine-robust aggregation (coordinate-wise median).
Joint use of privacy noise and robust aggregation disproportionately degrades detection of rare attacks relative to majority classes Part of performance collapse under strong privacy arises from training miscalibration, while a residual floor remains for ultra-rare categories even after epsilon-dependent tuning Geometric indistinguishability identified as a conceptual mechanism linking privacy-induced dispersion in client updates to loss of minority-class signal preservation during aggregation
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Early-stage empirical study on a systems-level problem using a single public dataset, identifying trade-offs rather than validating a solution; results are exploratory and motivate future work rather than settling a question.
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Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its deployment in operational environments must simultaneously satisfy three competing requirements: formal differential privacy guaranties, tolerance to Byzantine-adversarial participants, and reliable detection coverage across severely imbalanced attack categories. Existing literature treats these properties as independently composable, an assumption that this paper challenges both theoretically and empirically. In this paper, we study how these requirements interact in class-imbalanced federated NIDS and introduce geometric indistinguishability as a conceptual lens for a regime in which privacy-induced dispersion in client updates can make minority-class signals harder for robust aggregation to preserve. Using UNSW-NB15 as a case study, we evaluate DP-SGD combined with coordinate-wise median under label-flip and model-poisoning attacks, with threat coverage assessed across attack categories. Our results provide initial evidence that the joint use of privacy noise and robust aggregation can disproportionately degrade detection of rare attacks relative to majority classes. We also show that part of the observed collapse under strong privacy can arise from training miscalibration, while a residual performance floor may remain for ultra-rare categories even after epsilon-dependent tuning. These findings motivate studying privacy, robustness, and rare-attack coverage jointly rather than as independently composable properties, and suggest that aggregation-aware modeling and sample-aware evaluation are promising directions for trustworthy federated NIDS.
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