Multitentacle Federated Learning Over Software-Defined Industrial Internet of Things Against Adaptive Poisoning Attacks

Gaolei Li, Jun Wu*, Shenghong Li, Wu Yang, Changlian Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Software-defined industrial Internet of things (SD-IIoT) exploits federated learning to process the sensitive data at edges, while adaptive poisoning attacks threat the security of SD-IIoT. To address this problem, this article proposes a multi-tentacle federated learning (MTFL) framework, which is essential to guarantee the trustness of training data in SD-IIoT. In MTFL, participants with similar learning tasks are assigned to the same tentacle group. To identify adaptive poisoning attacks, a tentacle distribution-based efficient poisoning attack detection (TD-EPAD) algorithm is presented. And also, to minimize the impact of adaptive poisoning data, a stochastic tentacle data exchanging (STDE) protocol is also proposed. Simultaneously, to protect the tentacle's privacy in STDE, all exchanged data will be processed by differential privacy technology. A MTFL prototype system is implemented, which provides extensive ablation experiments and comparison experiments, demonstrating that the accuracy of the global model under attack scenario can be improved with 40%.

Original languageEnglish
Article number09772337
Pages (from-to)1260-1269
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number2
Publication statusPublished - 2023 Feb 1
Externally publishedYes


  • Differential privacy (DP)
  • multitentacle federated learning (MTFL)
  • poisoning attacks
  • software-defined industrial Internet of Things (SD-IIoT)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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