Article
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The Contribution of Federated Learning to AI Development
Version 1
: Received: 4 July 2024 / Approved: 5 July 2024 / Online: 5 July 2024 (16:04:10 CEST)
How to cite: Huang, S.; Diao, S.; Zhao, H.; Xu, L. The Contribution of Federated Learning to AI Development. Preprints 2024, 2024070551. https://doi.org/10.20944/preprints202407.0551.v1 Huang, S.; Diao, S.; Zhao, H.; Xu, L. The Contribution of Federated Learning to AI Development. Preprints 2024, 2024070551. https://doi.org/10.20944/preprints202407.0551.v1
Abstract
With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.
Keywords
Federation Learning; Privacy; Edge Artificial Intelligence; Medical Artificial Intelligence
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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