联合通信和计算设计的节能联邦边缘学习
Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design
摘要：
本文研究了一种联合边缘学习系统，在该系统中，边缘服务器协调一组边缘设备，根据它们本地分布的数据样本对共享的机器学习模型进行训练。在分布式训练过程中，采用通信和计算联合设计，对全局ML参数聚合的通信资源分配和局部ML参数更新的计算资源分配进行联合优化，提高了系统的能量效率.特别是，我们考虑了两个传输协议的边缘设备上传ML参数到边缘服务器，分别基于非正交多址和时分多址。在这两种协议下，我们通过联合优化边缘设备的传输功率和速率来最小化所有边缘设备在特定的有限训练持续时间内的总能耗，以满足给定的训练精度，用于上传ML参数及其中央处理器频率以进行本地更新。我们提出了有效的算法，最佳地解决制定的能量最小化问题，通过使用凸优化技术。数值结果表明，与其他基准计划相比，我们提出的联合通信和计算设计显着提高了联邦边缘学习系统的能源效率，通过适当平衡通信和计算之间的能量权衡。
英文摘要：
This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML parameters aggregation and the computation resource allocation for locally updating MLparameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML parameters to edge server, based on the non orthogonal multiple access and time division multiple access, respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update. We propose efficient algorithms to optimally solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly improves the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.
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