Article
Version 1
This version is not peer-reviewed
Blockchain Based Federated Learning Models Methods and Applications
Version 1
: Received: 2 July 2024 / Approved: 4 July 2024 / Online: 4 July 2024 (09:43:03 CEST)
How to cite: Sharma, R.; Sharma, K.; Patel, P. Blockchain Based Federated Learning Models Methods and Applications. Preprints 2024, 2024070417. https://doi.org/10.20944/preprints202407.0417.v1 Sharma, R.; Sharma, K.; Patel, P. Blockchain Based Federated Learning Models Methods and Applications. Preprints 2024, 2024070417. https://doi.org/10.20944/preprints202407.0417.v1
Abstract
This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.
Keywords
Federated Learning; Data Privacy Protection; Data Circulation; Blockchain Technology
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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