综述论文“Advances and Open Problems in Federated Learning”
2019年12月10日arXiv上载的联邦学习综述论文“Advances and Open Problems in Federated Learning“。 摘要：联邦学习（FL）是一种机器学习设置，其中许多客户端（例如移动设备或整个组织）在中央服务器（例如服务提供商）的协调下协同训练模型，同时保持训练数据的分散性。 FL体现了集中数据收集和最小化的原理，并且可以减轻由于传统的集中式机器学习和数据
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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. 论文笔记——Fair Resource Allocation in Federated Learning
- 4. Federated Learning for Vision-and-Language Grounding Problems阅读笔记
- 5. Federated Machine Learning:Concept and Applications(论文笔记)
- 6. Secure Federated Transfer Learning（论文笔记）
- 7. 论文阅读笔记2——献给新手的深度学习综述_Recent Advances in Deep Learning: An Overview
- 8. Measure Contribution of Participants in Federated Learning
- 9. 深度学习的最新进展：综述 Recent Advances in Deep Learning: An Overview
- 10. 综述论文“A Survey of Zero-Shot Learning: Settings, Methods, and Applications”