综述论文“Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection“
2019年12月3日上载到arXiv关于联邦学习的综述论文”A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection“。 摘要：联邦学习一直是在隐私限制下实现不同组织之间机器学习模型协作训练的热门研究领域。随着研究人员尝试使用不同的隐私保护方法来支持更多的机器学习
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相关文章
- 1. 综述论文“Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection“
- 2. Federated Learning for Vision-and-Language Grounding Problems
- 3. 综述论文“Advances and Open Problems in Federated Learning”
- 4. Federated Machine Learning:Concept and Applications(论文笔记)
- 5. Federated Learning for Vision-and-Language Grounding Problems阅读笔记
- 6. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data 论文阅读笔记
- 7. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 8. [论文解读] Callisto: Entropy based test generation and data quality assessment for Machine Learning ...
- 9. Secure Federated Transfer Learning（论文笔记）
- 10. Preparing for the General Data Protection Regulati