綜述論文「Advances and Open Problems in Federated Learning」
2019年12月10日arXiv上載的聯邦學習綜述論文「Advances and Open Problems in Federated Learning「。 摘要：聯邦學習（FL）是一種機器學習設置，其中許多客戶端（例如移動設備或整個組織）在中央服務器（例如服務提供商）的協調下協同訓練模型，同時保持訓練數據的分散性。 FL體現了集中數據收集和最小化的原理，並且可以減輕由於傳統的集中式機器學習和數據
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. 論文筆記——Fair Resource Allocation in Federated Learning
- 4. Federated Learning for Vision-and-Language Grounding Problems閱讀筆記
- 5. Federated Machine Learning:Concept and Applications(論文筆記)
- 6. Secure Federated Transfer Learning（論文筆記）
- 7. 論文閱讀筆記2——獻給新手的深度學習綜述_Recent Advances in Deep Learning: An Overview
- 8. Measure Contribution of Participants in Federated Learning
- 9. 深度學習的最新進展：綜述 Recent Advances in Deep Learning: An Overview
- 10. 綜述論文「A Survey of Zero-Shot Learning: Settings, Methods, and Applications」
- 更多相關文章...
- • SQL IN 操作符 - SQL 教程
- • Swift for-in 循環 - Swift 教程
- • RxJava操作符（七）Conditional and Boolean
- • Java Agent入門實戰（二）-Instrumentation源碼概述
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
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. 論文筆記——Fair Resource Allocation in Federated Learning
- 4. Federated Learning for Vision-and-Language Grounding Problems閱讀筆記
- 5. Federated Machine Learning:Concept and Applications(論文筆記)
- 6. Secure Federated Transfer Learning（論文筆記）
- 7. 論文閱讀筆記2——獻給新手的深度學習綜述_Recent Advances in Deep Learning: An Overview
- 8. Measure Contribution of Participants in Federated Learning
- 9. 深度學習的最新進展：綜述 Recent Advances in Deep Learning: An Overview
- 10. 綜述論文「A Survey of Zero-Shot Learning: Settings, Methods, and Applications」