论文标题
服务联邦学习和非养育学习用户：一种大型的MIMO方法
Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach
论文作者
论文摘要
及其数据隐私保护和沟通效率的联合学习（FL）被认为是超出5G/6G系统的有希望的学习框架。我们考虑一种场景，其中一组下行链路非FL用户与一组使用大量多输入多输出技术共同为FL用户共同服务。主要的挑战是如何利用资源为FL和非FL用户提供最佳服务。我们提出了一个通信方案，该方案可为非FL用户（UES）的下行链路和频段的每一半的FL UES的上行链路下行。我们制定了一个优化问题，以优化传输功率，以最大程度地提高非FL用户的最低有效数据速率，同时保证佛罗里达州用户每次FL通信的服务质量时间。然后，提出了连续的凸近似算法来解决该法式问题。数值结果证实，我们提出的方案明显胜过基线方案。
Federated learning (FL) with its data privacy protection and communication efficiency has been considered as a promising learning framework for beyond-5G/6G systems. We consider a scenario where a group of downlink non-FL users are jointly served with a group of FL users using massive multiple-input multiple-output technology. The main challenge is how to utilise the resource to optimally serve both FL and non-FL users. We propose a communication scheme that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in each half of the frequency band. We formulate an optimization problem for optimizing transmit power to maximize the minimum effective data rates for non-FL users, while guaranteeing a quality-of-service time of each FL communication round for FL users. Then, a successive convex approximation-based algorithm is proposed to solve the formulated problem. Numerical results confirm that our proposed scheme significantly outperforms the baseline scheme.
