Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 155 tok/s
Gemini 2.5 Pro 42 tok/s Pro
GPT-5 Medium 34 tok/s Pro
GPT-5 High 31 tok/s Pro
GPT-4o 101 tok/s Pro
Kimi K2 213 tok/s Pro
GPT OSS 120B 422 tok/s Pro
Claude Sonnet 4.5 36 tok/s Pro
2000 character limit reached

A Blockchain Solution for Collaborative Machine Learning over IoT (2311.14136v1)

Published 23 Nov 2023 in cs.LG, cs.CR, and cs.NI

Abstract: 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (28)
  1. doi:10.1109/MSP.2020.2975749.
  2. arXiv:1811.12470. URL http://arxiv.org/abs/1811.12470
  3. doi:10.1109/ACCESS.2016.2566339.
  4. doi:10.1109/ACCESS.2020.3037474.
  5. doi:10.1145/3551663.3558676.
  6. doi:10.1016/j.eswa.2022.119036.
  7. doi:10.1109/TII.2021.3085960.
  8. doi:10.1109/LCOMM.2019.2921755. URL https://www.scopus.com
  9. doi:10.1109/JIOT.2020.3017377. URL https://www.scopus.com
  10. doi:10.1109/IJCNN55064.2022.9892039.
  11. doi:10.1109/BalkanCom55633.2022.9900546.
  12. doi:10.1109/JIOT.2020.3032544.
  13. doi:10.1109/GLOBECOM42002.2020.9322159.
  14. doi:https://doi.org/10.1016/j.eswa.2023.119896.
  15. doi:10.1109/TII.2022.3170348.
  16. doi:10.1109/JIOT.2022.3206337.
  17. doi:10.1145/3560816.
  18. doi:10.1109/TII.2022.3215231.
  19. doi:10.1007/s00521-010-0511-4.
  20. arXiv:2203.13060.
  21. Geth - official go implementation of the ethereum protocol. URL https://geth.ethereum.org/
  22. Imagesegments dataset, River - Machine Learning for Data Streams. URL https://riverml.xyz/0.18.0/api/datasets/ImageSegments/
  23. Phishing dataset, River - Machine Learning for Data Streams. URL https://riverml.xyz/0.18.0/api/datasets/Phishing/
  24. Bananas dataset, River - Machine Learning for Data Streams. URL https://riverml.xyz/0.18.0/api/datasets/Bananas/
  25. scikit-learn, scikit-learn: Machine Learning in Python. URL https://scikit-learn.org/stable/
  26. Numpy, NumPy: The fundamental package for scientific computing with Python. URL https://numpy.org/
  27. River, River: Online machine learning in Python. URL https://riverml.xyz/0.18.0/
  28. Web3.py, Web3.py: A Python library for interacting with Ethereum. URL https://web3py.readthedocs.io/en/stable/

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube