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

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.

Automated summary

This article proposes a blockchain-based federated learning framework with Intel SGX-based trusted execution environment to securely aggregate local models in IIoTs. The framework leverages a blockchain network for secure model aggregation and tamper-proof storage. It uses SGX-enabled processors in each blockchain node to perform the aggregation tasks and ensure the authenticity and integrity of the aggregated model. Experimental evaluations are conducted to assess the performance of the proposed framework with different CNN models and datasets.