论文标题
解决机器学习中的现代和实用挑战：在线联盟和转移学习的调查
Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning
论文作者
论文摘要
在线联合学习（OFL）和在线转移学习（OTL）是两个协作范式，用于克服现代机器学习挑战，例如数据筒仓，流数据和数据安全。这项调查探讨了OTL和OTL在他们的主要进化途径中，以增强对在线联合和转移学习的理解。此外，在这项工作中强调了流行数据集的实际方面以及在线联合和转移学习的尖端应用程序。此外，这项调查提供了对潜在的未来研究领域的见解，并旨在为开发在线联盟和转移学习框架的专业人员提供资源。
Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming data, and data security. This survey explored OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning. Besides, practical aspects of popular datasets and cutting-edge applications for online federated and transfer learning are highlighted in this work. Furthermore, this survey provides insight into potential future research areas and aims to serve as a resource for professionals developing online federated and transfer learning frameworks.
