Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface

arXiv:2609.03420 · cs.CR, cs.AI, cs.LG · Submitted 2026-09-03 · Read on arXiv

cs.CR, cs.AI, cs.LG

Submitted: 2026-09-03

Updated: 2026-09-03

Terminology

Aggregation — 34× in this paper · explained in 1 episode
Robustness — 18× in this paper · explained in 5 episodes
Calibration — 17× in this paper · explained in 7 episodes
Federated Learning — 13× in this paper · explained in 14 episodes
Differential Privacy — 11× in this paper · explained in 4 episodes
Differentially Private — 7× in this paper · explained in 3 episodes
Accuracy — 3× in this paper · explained in 1 episode
Disparate Impact — 3× in this paper
Adversarial Robustness — 2× in this paper · explained in 3 episodes
Benchmark — 2× in this paper · explained in 2 episodes
F1 Score — 2× in this paper · explained in 3 episodes
Generalization — 2× in this paper · explained in 13 episodes
Accountability — 1× in this paper · explained in 2 episodes
Adversarial scenarios — 1× in this paper · explained in 1 episode
Aggregation Function — 1× in this paper · explained in 1 episode
Consistency — 1× in this paper · explained in 3 episodes
Cortical Memory System — 1× in this paper
Data Heterogeneity — 1× in this paper · explained in 3 episodes
Evaluation Dimensions — 1× in this paper · explained in 3 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
Federated Learning (FL) — 1× in this paper · explained in 7 episodes
Machine-learning-based Network Intrusion Detection Systems — 1× in this paper
Stochastic Gradient Descent — 1× in this paper
Stochastic Gradient Descent (SGD) — 1× in this paper · explained in 5 episodes
University of New South Wales — 1× in this paper

Sources

Differentially Private Federated Learning: A Systematic Review

Terminology

Sources

Related papers