Federated learning (FL) is a framework which allows multiple users to jointly
train a global machine learning (ML) model by transmitting only model updates
under the coordination of a parameter server, while being able to keep their
datasets local. One key motivation of such distributed frameworks is to provide
privacy guarantees to the users. However, preserving the users' datasets
locally is shown to be not sufficient for privacy. Several differential privacy
(DP) mechanisms have been proposed to provide provable privacy guarantees by
introducing randomness into the framework, and majority of these mechanisms
rely on injecting additive noise. FL frameworks also face the challenge of
communication efficiency, especially as machine learning models grow in
complexity and size. Quantization is a commonly utilized method, reducing the
communication cost by transmitting compressed representation of the underlying
information. Although there have been several studies on DP and quantization in
FL, the potential contribution of the quantization method alone in providing
privacy guarantees has not been extensively analyzed yet. We in this paper
present a novel stochastic quantization method, utilizing a mixed geometric
distribution to introduce the randomness needed to provide DP, without any
additive noise. We provide convergence analysis for our framework and
empirically study its performance.

本文提出了一种新颖的随机量化方法，利用混合几何分布引入所需的随机性，以提供差分隐私，而不引入任何附加噪声，并通过收敛分析和实证研究了该方法的性能。

QMGeo：基于混合截断几何分布的随机量化差分隐私联邦学习

QMGeo: Differentially Private Federated Learning via Stochastic  Quantization with Mixed Truncated Geometric Distribution

3D Gaussian Splatting is a new method for modeling and rendering 3D radiance
fields that achieves much faster learning and rendering time compared to SOTA
NeRF methods. However, it comes with a drawback in the much larger storage
demand compared to NeRF methods since it needs to store the parameters for
several 3D Gaussians. We notice that many Gaussians may share similar
parameters, so we introduce a simple vector quantization method based on
\kmeans algorithm to quantize the Gaussian parameters. Then, we store the small
codebook along with the index of the code for each Gaussian. Moreover, we
compress the indices further by sorting them and using a method similar to
run-length encoding. We do extensive experiments on standard benchmarks as well
as a new benchmark which is an order of magnitude larger than the standard
benchmarks. We show that our simple yet effective method can reduce the storage
cost for the original 3D Gaussian Splatting method by a factor of almost
$20\times$ with a very small drop in the quality of rendered images.

用基于 k-means 算法的简单向量量化方法和类似于游程编码的方法来减少 3D 高斯雨滴方法的存储成本，并在质量下降很小的情况下减少存储成本近 20 倍。

Compact3D: 压缩高斯散射辐射场模型与矢量量化

Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector  Quantization

3D whole-body human mesh recovery aims to reconstruct the 3D human body,
face, and hands from a single image. Although powerful deep learning models
have achieved accurate estimation in this task, they require enormous memory
and computational resources. Consequently, these methods can hardly be deployed
on resource-limited edge devices. In this work, we propose a Binarized Dual
Residual Network (BiDRN), a novel quantization method to estimate the 3D human
body, face, and hands parameters efficiently. Specifically, we design a basic
unit Binarized Dual Residual Block (BiDRB) composed of Local Convolution
Residual (LCR) and Block Residual (BR), which can preserve full-precision
information as much as possible. For LCR, we generalize it to four kinds of
convolutional modules so that full-precision information can be propagated even
between mismatched dimensions. We also binarize the face and hands
box-prediction network as Binaried BoxNet, which can further reduce the model
redundancy. Comprehensive quantitative and qualitative experiments demonstrate
the effectiveness of BiDRN, which has a significant improvement over
state-of-the-art binarization algorithms. Moreover, our proposed BiDRN achieves
comparable performance with full-precision method Hand4Whole while using just
22.1% parameters and 14.8% operations. We will release all the code and
pretrained models.

通过使用 Binarized Dual Residual Network (BiDRN) 量化方法，本文在资源有限的边缘设备上以 22.1% 的参数和 14.8% 的运算量实现了与全精度方法 Hand4Whole 相当的性能，且在 3D 全身人体网格恢复任务中显著提高了与最先进的二值化算法相比的效果。