Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Study formalises the privacy–robustness–fairness trade-off in federated intrusion detection using geometric indistinguishability, offering a tuning framework for multi-party SOC collaboration.
Summary written by editorial AI · Source link below
arXiv:2609.03420v1 Announce Type: new Abstract: 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
Editorial Analysis
Federated IDS deployments across organisational boundaries must balance privacy guarantees with detection accuracy—this framework quantifies that trade-off.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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