用格码实现无线联邦学习，抗干扰且精度更高
Compute-Update Federated Learning: A Lattice Coding Approach Over-the-Air
- 用格码同时量化参数并利用设备间干扰
- 无需信道状态信息，仍可稳定聚合模型参数
- 适合大规模无线边缘计算场景的高效学习
本文提出一种基于数字通信的无线联邦学习框架，采用新型联合源信道编码方案实现空中计算。该方案不依赖设备端的信道状态信息，利用格码对模型参数进行量化，并有效利用设备间的信号干扰。在服务器端设计了一种新型接收结构，可可靠解码量化参数的整数组合作为格点以完成聚合。通过数学方法推导了所提方案的收敛性边界，并提出一种聚合指标及对应算法，用于每轮通信中确定有效的整数系数。实验结果表明，无论信道动态或数据异构性如何，该方案在各类参数下均保持较高学习精度，显著优于其他无线计算方法。
原文摘要 · Abstract (English)
This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattice codes to both quantize model parameters and exploit interference from the devices. We propose a novel receiver structure at the server, designed to reliably decode an integer combination of the quantized model parameters as a lattice point for the purpose of aggregation. We present a mathematical approach to derive a convergence bound for the proposed scheme and offer design remarks. In this context, we suggest an aggregation metric and a corresponding algorithm to determine effective integer coefficients for the aggregation in each communication round. Our results illustrate that, regardless of channel dynamics and data heterogeneity, our scheme consistently delivers superior learning accuracy across various parameters and markedly surpasses other over-the-air methodologies.
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