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

This paper addresses the challenge of building intrusion detection systems using federated learning while maintaining privacy, robustness, and fairness—critical requirements for distributed security in regulated environments. The authors propose a geometric indistinguishability approach at the aggregation interface to prevent adversaries from inferring sensitive information about participating organizations' network data during model training.

This paper addresses the challenge of building intrusion detection systems using federated learning while maintaining privacy, robustness, and fairness—critical requirements for distributed security in regulated environments. The authors propose a geometric indistinguishability approach at the aggregation interface to prevent adversaries from inferring sensitive information about participating organizations' network data during model training. The work is relevant for enterprises managing distributed infrastructure who must balance collaborative threat detection with data protection obligations.

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