In federated learning, a server must periodically broadcast a model to the
agents. We propose to use multi-resolution coding and modulation (also known as
non-uniform modulation) for this purpose. In the simplest instance, broadcast
transmission is used, whereby all agents are targeted with one and the same
transmission (typically without any particular favored beam direction), which
is coded using multi-resolution coding/modulation. This enables high-SNR
agents, with high path gains to the server, to receive a more accurate model
than the low-SNR agents do, without consuming more downlink resources. As one
implementation, we use transmission with a non-uniform 8-PSK constellation,
where a high-SNR receiver (agent) can separate all 8 constellation points
(hence receive 3 bits) whereas a low-SNR receiver can only separate 4 points
(hence receive 2 bits). By encoding the least significant information in the
third bit, the high-SNR receivers can obtain the model with higher accuracy,
while the low-SNR receiver can still obtain the model although with reduced
accuracy, thereby facilitating at least some basic participation of the low-SNR
receiver. We show the effectiveness of our proposed scheme via experimentation
using federated learning with the MNIST data-set.

在联邦学习中，我们提出使用多分辨率编码和调制（也称为非均匀调制）来将模型定期广播给代理服务器，以便高信噪比的接收器可以接收到更准确的模型，而低信噪比的接收器仍然可以以降低的精度接收模型。

多分辨率模型广播的联邦学习

Federated Learning with Multi-resolution Model Broadcast

This work centers on the communication aspects of decentralized learning over
wireless networks, using consensus-based decentralized stochastic gradient
descent (D-SGD). Considering the actual communication cost or delay caused by
in-network information exchange in an iterative process, our goal is to achieve
fast convergence of the algorithm measured by improvement per transmission
slot. We propose BASS, an efficient communication framework for D-SGD over
wireless networks with broadcast transmission and probabilistic subgraph
sampling. In each iteration, we activate multiple subsets of non-interfering
nodes to broadcast model updates to their neighbors. These subsets are randomly
activated over time, with probabilities reflecting their importance in network
connectivity and subject to a communication cost constraint (e.g., the average
number of transmission slots per iteration). During the consensus update step,
only bi-directional links are effectively preserved to maintain communication
symmetry. In comparison to existing link-based scheduling methods, the inherent
broadcasting nature of wireless channels offers intrinsic advantages in
speeding up convergence of decentralized learning by creating more communicated
links with the same number of transmission slots.

该研究探讨了分布式学习在无线网络中的通信方面，使用基于共识的分布式随机梯度下降法（D-SGD）。为了实现算法的快速收敛性，我们提出了一种名为 BASS 的高效通信框架，其中利用广播传输和概率子图抽样，在每次迭代中激活多个无干扰节点的子集将模型更新广播给它们的邻居。与现有的基于链路调度方法相比，在无线信道的广播特性下，利用相同数量的传输时间槽创建更多的通信链路能够加快分布式学习的收敛速度。

基于广播的子图采样的无线网络分布式学习

Decentralized Learning over Wireless Networks with Broadcast-Based  Subgraph Sampling

In this work, we focus on the communication aspect of decentralized learning,
which involves multiple agents training a shared machine learning model using
decentralized stochastic gradient descent (D-SGD) over distributed data. In
particular, we investigate the impact of broadcast transmission and
probabilistic random access policy on the convergence performance of D-SGD,
considering the broadcast nature of wireless channels and the link dynamics in
the communication topology. Our results demonstrate that optimizing the access
probability to maximize the expected number of successful links is a highly
effective strategy for accelerating the system convergence.

本研究侧重于分散学习的通信方面，探讨了广播传输和概率随机访问策略对分布式随机梯度下降 (D-SGD) 收敛性能的影响，结果表明，优化访问概率能够极大地加速系统的收敛速度。