论文标题
半充实的学习
Semi-Federated Learning
论文作者
论文摘要
联合学习（FL）使大量的分布式信息和通信技术（ICT）设备可以学习全球共识模型，而没有任何参与者向中央服务器揭示自己的数据。但是，佛罗里达州的实用性，沟通费用以及非独立和相同的分布（非IID）数据挑战仍然需要关注。在这项工作中，我们提出了半养育学习（半FL），该学习在两个方面与FL不同，当地客户聚类和集群内培训。顺序培训方式是为我们在本文中的集群培训而设计的，该培训使邻近的客户能够共享他们的学习模型。提出的半FL可以轻松地应用于未来的移动通信网络，并且需要更少的上链接传输带宽。数值实验验证了所提出的半FL的非IID数据的可行性，学习绩效和鲁棒性。半FL扩展了FL的现有潜力。
Federated learning (FL) enables massive distributed Information and Communication Technology (ICT) devices to learn a global consensus model without any participants revealing their own data to the central server. However, the practicality, communication expense and non-independent and identical distribution (Non-IID) data challenges in FL still need to be concerned. In this work, we propose the Semi-Federated Learning (Semi-FL) which differs from the FL in two aspects, local clients clustering and in-cluster training. A sequential training manner is designed for our in-cluster training in this paper which enables the neighboring clients to share their learning models. The proposed Semi-FL can be easily applied to future mobile communication networks and require less up-link transmission bandwidth. Numerical experiments validate the feasibility, learning performance and the robustness to Non-IID data of the proposed Semi-FL. The Semi-FL extends the existing potentials of FL.
