块链
计算机科学
Guard(计算机科学)
分布式计算
受信任的网络连接
骨料(复合)
互联网
计算机安全
可信计算
物联网
计算机网络
操作系统
程序设计语言
复合材料
材料科学
作者
Aditya Pribadi Kalapaaking,Ibrahim Khalil,Mohammad Saidur Rahman,Mohammed Atiquzzaman,Xun Yi,Mahathir Almashor
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:19 (2): 1703-1714
被引量:21
标识
DOI:10.1109/tii.2022.3170348
摘要
This article proposes a blockchain-based federated learning (FL) framework with Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) to securely aggregate local models in Industrial Internet-of-Things (IIoTs). In FL, local models can be tampered with by attackers. Hence, a global model generated from the tampered local models can be erroneous. Therefore, the proposed framework leverages a blockchain network for secure model aggregation. Each blockchain node hosts an SGX-enabled processor that securely performs the FL-based aggregation tasks to generate a global model. Blockchain nodes can verify the authenticity of the aggregated model, run a blockchain consensus mechanism to ensure the integrity of the model, and add it to the distributed ledger for tamper-proof storage. Each cluster can obtain the aggregated model from the blockchain and verify its integrity before using it. We conducted several experiments with different CNN models and datasets to evaluate the performance of the proposed framework.
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