綜述論文「Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection「
2019年12月3日上載到arXiv關於聯邦學習的綜述論文」A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection「。 摘要：聯邦學習一直是在隱私限制下實現不同組織之間機器學習模型協作訓練的熱門研究領域。隨着研究人員嘗試使用不同的隱私保護方法來支持更多的機器學習
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相關文章
- 1. 綜述論文「Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection「
- 2. Federated Learning for Vision-and-Language Grounding Problems
- 3. 綜述論文「Advances and Open Problems in Federated Learning」
- 4. Federated Machine Learning:Concept and Applications(論文筆記)
- 5. Federated Learning for Vision-and-Language Grounding Problems閱讀筆記
- 6. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 論文閱讀筆記
- 7. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 8. [論文解讀] Callisto: Entropy based test generation and data quality assessment for Machine Learning ...
- 9. Secure Federated Transfer Learning（論文筆記）
- 10. Preparing for the General Data Protection Regulati