Abstract
The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.
Abstract (translated)
无线通信的质量将直接影响联邦学习的性能，因此本文通过符号误差率（SER）分析了无线通信对FL的影响。在FL系统中，非正交多接入（NOMA）可以作为基本通信框架，以减少多个用户引起的通信拥塞和干扰，利用无线信道的叠加特性。基于序列干扰消除（SIC）的最低均方误差（MMSE）技术被用来逐个恢复接收端每个终端节点的梯度。在本文中，将梯度参数量化为多个比特，以保留更多的梯度信息，并提高传输错误的容错性。基于此，我们设计了SER-based设备选择机制（SER-DSM），以确保学习性能不受通信条件差的用户的影响，同时容纳尽可能多的用户参与学习过程，这是某种程度上的包容。实验结果表明，多比特量化梯度对FL的影响以及基于SER的设备选择机制的必要性和优越性。
URL
https://arxiv.org/abs/2405.02320
