mlsanityData to 14 Oct 2026

PVBRF-ID: Private, Verifiable and Byzantine-Robust Federated Intrusion Detection for Oilfield Enterprise Networks

arXiv 2610.12704 ↗First posted 8 Oct 2026cs.CR cs.LGcited by 0

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Abstract (arXiv)

Federated learning allows dispersed oilfield sites to train a shared intrusion detector without pooling network traffic, yet its aggregation step forces a choice between privacy and robustness. Byzantine-robust rules must read each update in the clear, whereas secure aggregation hides updates from the checks that detect poisoning. We present PVBRF-ID, a two-server protocol in which sites secret-share their updates and the servers jointly test each update's norm and its cosine similarity to a reference computed on a small clean root set, revealing one accept bit per site. Commitments, Merkle roots and a homomorphic audit make the inclusion of accepted updates publicly verifiable. On a 15-class Edge-IIoTset task with ten sites holding differently distributed data and ten random seeds, PVBRF-ID matched FedAvg on clean data (macro-F1 0.708 vs. 0.706) and withstood sign-flip attacks (0.696 vs. 0.091). Against an adaptive attacker controlling 30% of sites, PVBRF-ID with secure clipping reached 0.641, whereas FedAvg fell to 0.046; plaintext multi-Krum remained higher by 0.052. An update-inference attacker recovered each site's class mix from plaintext updates (Spearman correlation 0.81) but not from one server's view. Backdoors passed the filter, and noise-based mitigation gave only heuristic protection. The implementation uses Python 3.13, NumPy 2.3, 30 rounds and 10 seeds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 35 references that are in the map. First posted on arXiv 8 Oct 2026.

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14 of 35 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Deep Learning with Differential Privacycited by 2,728
  3. 2019Deep Leakage from Gradientscited by 1,021
  4. 2018How To Backdoor Federated Learningcited by 765
  5. 2018Byzantine-Robust Distributed Learning: Towards Optimal Statistical Ratescited by 711
  6. 2017Renyi Differential Privacycited by 650
  7. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  8. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 493
  9. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  10. 2019Can You Really Backdoor Federated Learning?cited by 255
  11. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  12. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 166
2 more
  1. 2017Prio: Private, Robust, and Scalable Computation of Aggregate Statisticscited by 36
  2. 2021RoFL: Robustness of Secure Federated Learningcited by 25

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  8. 2021FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning