We propose a sparse and privacy-enhanced representation for Human Pose
Estimation (HPE). Given a perspective camera, we use a proprietary motion
vector sensor(MVS) to extract an edge image and a two-directional motion vector
image at each time frame. Both edge and motion vector images are sparse and
contain much less information (i.e., enhancing human privacy). We advocate that
edge information is essential for HPE, and motion vectors complement edge
information during fast movements. We propose a fusion network leveraging
recent advances in sparse convolution used typically for 3D voxels to
efficiently process our proposed sparse representation, which achieves about
13x speed-up and 96% reduction in FLOPs. We collect an in-house edge and motion
vector dataset with 16 types of actions by 40 users using the proprietary MVS.
Our method outperforms individual modalities using only edge or motion vector
images. Finally, we validate the privacy-enhanced quality of our sparse
representation through face recognition on CelebA (a large face dataset) and a
user study on our in-house dataset.

我们提出了一种稀疏和增强隐私保护的人体姿态估计方法，使用专有的运动矢量传感器提取边缘图像和双向运动矢量图像，并结合稀疏卷积网络进行高效处理，通过边缘信息和运动矢量图像相结合，我们的方法在速度和运算量方面取得了巨大改进，验证了我们提出的稀疏表示的隐私保护质量。

人体姿态估计的稀疏和隐私增强表示

Sparse and Privacy-enhanced Representation for Human Pose Estimation

Federated learning (FL) is a collaborative learning paradigm for
decentralized private data from mobile terminals (MTs). However, it suffers
from issues in terms of communication, resource of MTs, and privacy. Existing
privacy-preserving FL methods usually adopt the instance-level differential
privacy (DP), which provides a rigorous privacy guarantee but with several
bottlenecks: severe performance degradation, transmission overhead, and
resource constraints of edge devices such as MTs. To overcome these drawbacks,
we propose Fed-LTP, an efficient and privacy-enhanced FL framework with
\underline{\textbf{L}}ottery \underline{\textbf{T}}icket
\underline{\textbf{H}}ypothesis (LTH) and zero-concentrated
D\underline{\textbf{P}} (zCDP). It generates a pruned global model on the
server side and conducts sparse-to-sparse training from scratch with zCDP on
the client side. On the server side, two pruning schemes are proposed: (i) the
weight-based pruning (LTH) determines the pruned global model structure; (ii)
the iterative pruning further shrinks the size of the pruned model's
parameters. Meanwhile, the performance of Fed-LTP is also boosted via model
validation based on the Laplace mechanism. On the client side, we use
sparse-to-sparse training to solve the resource-constraints issue and provide
tighter privacy analysis to reduce the privacy budget. We evaluate the
effectiveness of Fed-LTP on several real-world datasets in both independent and
identically distributed (IID) and non-IID settings. The results clearly confirm
the superiority of Fed-LTP over state-of-the-art (SOTA) methods in
communication, computation, and memory efficiencies while realizing a better
utility-privacy trade-off.

提出了 Fed-LTP，它是一种高效的隐私增强的联邦学习框架，能够通过稀疏到稀疏训练解决资源限制问题，并使用两种修剪方案（即基于权重的修剪和迭代修剪）生成被修剪的全局模型，拥有更好的通信、计算和存储效率，并采用基于拉普拉斯机制的模型验证实现更好的收益隐私权衡。