Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things

【Author】 Kalapaaking, Aditya Pribadi; Khalil, Ibrahim; Rahman, Mohammad Saidur; Atiquzzaman, Mohammed; Yi, Xun; Almashor, Mahathir

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【Abstract】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.

【Keywords】Blockchain; deep learning; federated learning (FL); Intel Software Guard Extension (SGX); Internet-of-Things (IoT); secure aggregation; trusted execution environment (TEE)

【发表时间】2023 FEB

【收录时间】2023-03-24

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习