Blockchain-based Federated Learning with Secure Aggregation in Trusted
Execution Environment for Internet-of-Things
- URL: http://arxiv.org/abs/2304.12889v1
- Date: Tue, 25 Apr 2023 15:00:39 GMT
- Title: Blockchain-based Federated Learning with Secure Aggregation in Trusted
Execution Environment for Internet-of-Things
- Authors: Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman,
Mohammed Atiquzzaman, Xun Yi, and Mahathir Almashor
- Abstract summary: This paper 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.
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.
- Score: 20.797220195954065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper 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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