论文标题
先知：通过预测培训和报告阶段的质量，积极主动进行联邦学习
Prophet: Proactive Candidate-Selection for Federated Learning by Predicting the Qualities of Training and Reporting Phases
论文作者
论文摘要
尽管设备连接的挑战在5G网络中大大减轻了，但训练潜伏期仍然是阻止联邦学习（FL）在很大程度上被采用的障碍。导致延迟较大的最根本问题之一是FL的不良候选人选择。在动态环境中，由现有反应性候选序列算法选择的移动设备很可能无法完成FL的培训和报告阶段，因为FL参数服务器只知道所有候选者的当前观察到的资源。为此，我们在本文中研究了FL的主动候选选择。我们首先让每个候选设备使用LSTM在本地预测其培训和报告阶段的品质。然后，提出的候选算法由深度加固学习（DRL）框架实现。最后，现实世界中的痕量驱动实验证明，所提出的方法的表现优于现有的反应算法
Although the challenge of the device connection is much relieved in 5G networks, the training latency is still an obstacle preventing Federated Learning (FL) from being largely adopted. One of the most fundamental problems that lead to large latency is the bad candidate-selection for FL. In the dynamic environment, the mobile devices selected by the existing reactive candidate-selection algorithms very possibly fail to complete the training and reporting phases of FL, because the FL parameter server only knows the currently-observed resources of all candidates. To this end, we study the proactive candidate-selection for FL in this paper. We first let each candidate device predict the qualities of both its training and reporting phases locally using LSTM. Then, the proposed candidateselection algorithm is implemented by the Deep Reinforcement Learning (DRL) framework. Finally, the real-world trace-driven experiments prove that the proposed approach outperforms the existing reactive algorithms
