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A Blockchain Solution for Collaborative Machine Learning over IoT

arXiv 2311.14136 ↗First posted 23 Nov 2023cs.LG cs.CR cs.NIcited by 0

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Abstract (arXiv)

The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showcasing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 23 Nov 2023.

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17 of 58 references.

References · 17

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
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  3. 2020The Future of Digital Health with Federated Learningcited by 625
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  6. 2018Analyzing Federated Learning through an Adversarial Lenscited by 346
  7. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 266
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  9. 2018Mitigating Sybils in Federated Learning Poisoningcited by 158
  10. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 104
  11. 2020River: machine learning for streaming data in Pythoncited by 96
  12. 2020A Blockchain-based Decentralized Federated Learning Framework with Committee Consensuscited by 83
5 more
  1. 2022Cross-Silo Federated Learning: Challenges and Opportunitiescited by 38
  2. 2022SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in Secure Aggregation for Federated Learningcited by 18
  3. 2022AI for Next Generation Computing: Emerging Trends and Future Directionscited by 13
  4. 2023Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Thingscited by 10
  5. 2023Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verificationcited by 4

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