Multi-Tentacle Federated Learning over Software-Defined Industrial Internet of Things Against Adaptive Poisoning Attacks

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

研究成果: Article査読

抄録

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 paper 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%.

本文言語English
ページ(範囲)1
ページ数1
ジャーナルIEEE Transactions on Industrial Informatics
DOI
出版ステータスAccepted/In press - 2022

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 情報システム
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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