Privacy-Preserving and Reliable Decentralized Federated Learning

Gao, Yuanyuan; Zhang, Lei; Wang, Lulu; Choo, Kim‐Kwang Raymond; Zhang, Rui

doi:10.1109/tsc.2023.3250705

Cited by 11 publications

(2 citation statements)

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“…In addressing the vulnerabilities associated with model performance and data privacy in FL systems, various research efforts have been undertaken. For instance, a quality-based aggregation method combined with local differential privacy is proposed to preserve both model accuracy and data privacy amid potential adversarial attacks on FL, as demonstrated in [15]. A systematic analysis of secure FL applications outlined in [16] highlights the significance of countering security threats to uphold user privacy and model integrity.…”

Section: Related Workmentioning

confidence: 99%

“…FL's decentralized nature not only ensures the efficient processing of distributed IIoT data but also addresses some of the most pressing privacy concerns by allowing numerous compute nodes to collaboratively hone a shared global model without exposing individual datasets [1], [2]. In addition, FL leverages distributed computational resources to overcome the limitations of a central server, thereby enhancing scalability and responsiveness in IIoT environments [3], [4]. However, while FL is inherently more privacy-preserving than centralized approaches, it is not immune to security threats, such as model poisoning and Sybil attacks, which jeopardize the integrity of collective model training in the IIoT context [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Fortifying Federated Learning in IIoT: Leveraging Blockchain and Digital Twin Innovations for Enhanced Security and Resilience

Prathiba,

Govindarajan,

Pranav Amirtha Ganesan

et al. 2024

IEEE Access

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Ensuring robustness against adversarial attacks is imperative for Machine Learning (ML) systems within the critical infrastructures of the Industrial Internet of Things (IIoT). This paper addresses vulnerabilities in IIoT systems, particularly in distributed environments like Federated Learning (FL) by presenting a resilient framework -Secure Federated Learning (SFL) specifically designed to mitigate data and model poisoning, as well as Sybil attacks within these networks. Sybil attacks, involving the creation of multiple fake identities, and poisoning attacks significantly compromise the integrity and reliability of ML models in FL environments. Our SFL framework leverages a Digital Twin (DT) as a critical aggregation checkpoint to counteract data and model poisoning attacks in IIoT's distributed settings. The DT serves as a protective mechanism during the model update aggregation phase, substantially enhancing the system's resilience. To further secure IIoT infrastructures, SFL employs blockchain-based Non-Fungible Tokens (NFTs) to authenticate participant identities, effectively preventing Sybil attacks by ensuring traceability and accountability among distributed nodes. Experimental evaluation within IIoT scenarios demonstrates that SFL substantially enhances defensive capabilities, maintaining the integrity and robustness of model learning. Comparative results reveal that the SFL framework, when applied to IIoT federated environments, achieves a commendable 97% accuracy, outperforming conventional FL approaches. SFL also demonstrates a remarkable reduction in loss rate, recording just 0.07 compared to the 0.14 loss rate experienced by standard FL systems. These findings highlight the efficiency and applicability of the SFL framework in enhancing data security and traceability within the IIoT ecosystem.

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Section: Related Workmentioning

confidence: 99%