Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure
Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. However, existing defenses operate strictly in update space, leaving aggregators blind to data poisoning; model updates derived from fabricated telemetry remain indistinguishable from honest contributions. We repurpose cyber-physical process invariants, such as conservation laws and actuator couplings, from runtime detection heuristics into a verifiable admission requirement for federated updates, mined automatically from clean operational data. We evaluate this admission gate across two physical water testbeds (SWaT, WADI) and a distribution ben...
| Authors | Jeff Nijsse, Shu Su, Benjamin Oholeguy et al. |
|---|---|
| Primary category | cs.CR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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