Federated learning (FL) is a novel approach to machine learning that allows
multiple edge devices to collaboratively train a model without disclosing their
raw data. However, several challenges hinder the practical implementation of
this approach, especially when devices and the server communicate over wireless
channels, as it suffers from communication and computation bottlenecks in this
case. By utilizing a communication-efficient framework, we propose a novel
zero-order (ZO) method with a one-point gradient estimator that harnesses the
nature of the wireless communication channel without requiring the knowledge of
the channel state coefficient. It is the first method that includes the
wireless channel in the learning algorithm itself instead of wasting resources
to analyze it and remove its impact. The two main difficulties of this work are
that in FL, the objective function is usually not convex, which makes the
extension of FL to ZO methods challenging, and that including the impact of
wireless channels requires extra attention. However, we overcome these
difficulties and comprehensively analyze the proposed zero-order federated
learning (ZOFL) framework. We establish its convergence theoretically, and we
prove a convergence rate of $O(\frac{1}{\sqrt[3]{K}})$ in the nonconvex
setting. We further demonstrate the potential of our algorithm with
experimental results, taking into account independent and identically
distributed (IID) and non-IID device data distributions.

利用通信高效的框架，我们提出了一种新颖的、基于零阶方法的联邦学习（ZOFL）框架，通过利用无线通信信道的特性，在学习算法本身中包含无线信道，而无需分析并消除其影响。

渲染有用的无线环境：一种零阶随机联邦学习方法

Rendering Wireless Environments Useful for Gradient Estimators: A  Zero-Order Stochastic Federated Learning Method

3D motion estimation including scene flow and point cloud registration has
drawn increasing interest. Inspired by 2D flow estimation, recent methods
employ deep neural networks to construct the cost volume for estimating
accurate 3D flow. However, these methods are limited by the fact that it is
difficult to define a search window on point clouds because of the irregular
data structure. In this paper, we avoid this irregularity by a simple yet
effective method.We decompose the problem into two interlaced stages, where the
3D flows are optimized point-wisely at the first stage and then globally
regularized in a recurrent network at the second stage. Therefore, the
recurrent network only receives the regular point-wise information as the
input. In the experiments, we evaluate the proposed method on both the 3D scene
flow estimation and the point cloud registration task. For 3D scene flow
estimation, we make comparisons on the widely used FlyingThings3D and
KITTIdatasets. For point cloud registration, we follow previous works and
evaluate the data pairs with large pose and partially overlapping from
ModelNet40. The results show that our method outperforms the previous method
and achieves a new state-of-the-art performance on both 3D scene flow
estimation and point cloud registration, which demonstrates the superiority of
the proposed zero-order method on irregular point cloud data.

本文提出一种基于递归网络的零阶方法，可用于解决对不规则点云数据进行 3D 场景流估计和点云配准两项任务，实验结果表明该方法能够超越以往方法并达到新的最佳表现。