论文标题
渠道驱动的去中心化贝叶斯联合学习D2D网络中可信赖的决策
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks
论文作者
论文摘要
贝叶斯联邦学习（FL）提供了一个原则上的框架，以说明实施协作培训的节点可用数据中限制引起的不确定性。在贝叶斯FL中，节点在模型参数空间上交换了有关局部后分布的信息。本文着重于通过分散的随机梯度Langevin Dynamics（DSGLD）在设备到设备（D2D）网络中实现的贝叶斯FL，这是最近引入的一种基于梯度的Markov Chain Monte Carlo（MCMC）方法。基于DSGLD应用模型参数的随机高斯扰动的观察，我们建议利用D2D链接上的通道噪声作为MCMC采样机制。将所提出的方法与基于压缩和数字传输的常规频率FL进行比较，突出了优势和局限性。
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training. In Bayesian FL, nodes exchange information about local posterior distributions over the model parameters space. This paper focuses on Bayesian FL implemented in a device-to-device (D2D) network via Decentralized Stochastic Gradient Langevin Dynamics (DSGLD), a recently introduced gradient-based Markov Chain Monte Carlo (MCMC) method. Based on the observation that DSGLD applies random Gaussian perturbations of model parameters, we propose to leverage channel noise on the D2D links as a mechanism for MCMC sampling. The proposed approach is compared against a conventional implementation of frequentist FL based on compression and digital transmission, highlighting advantages and limitations.
