mlsanityData to 7 Oct 2026

Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things

arXiv 2304.12889 ↗First posted 25 Apr 2023cs.CR cs.AIcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 25 Apr 2023.

Builds on

18 of 42 references.

References · 18

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6 more
  1. 2018Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardwarecited by 101
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 2; 2026: 2.

10 papers.

  1. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  2. 2023Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchaincited by 3
  3. 2023A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimizationcited by 0
  4. 2023A Blockchain Solution for Collaborative Machine Learning over IoTcited by 0
  5. 2024Auditable Homomorphic-based Decentralized Collaborative AI with Attribute-based Differential Privacycited by 0
  6. 2024Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learningcited by 0
  7. 2025Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updatescited by 0
  8. 2025FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learningcited by 0
  9. 2026FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modelingcited by 0
  10. 2026Upholding Robustness in Federated Learning: Trends, Emerging Strategies, and Research Opportunitiescited by 0