כתבה
arXiv cs.LG ·
פרטיות, רובוסטנציה והוגנות: תרחישי סחיטה בלמידת רשת פדרטיבית
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
למידת רשת פדרטיבית: תרחישי סחיטה בין פרטיות, רובוסטנציה והוגנות בהגנה מפני התקפות רשת.
תקציר מקורי באנגליתarXiv:2609.03420v1 Announce Type: cross 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 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-induc
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arxiv.org
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