Deep neural networks (DNNs) often fail silently with over-confident
predictions on out-of-distribution (OOD) samples, posing risks in real-world
deployments. Existing techniques predominantly emphasize either the feature
representation space or the gradient norms computed with respect to DNN
parameters, yet they overlook the intricate gradient distribution and the
topology of classification regions. To address this gap, we introduce
GRadient-aware Out-Of-Distribution detection in interpolated manifolds (GROOD),
a novel framework that relies on the discriminative power of gradient space to
distinguish between in-distribution (ID) and OOD samples. To build this space,
GROOD relies on class prototypes together with a prototype that specifically
captures OOD characteristics. Uniquely, our approach incorporates a targeted
mix-up operation at an early intermediate layer of the DNN to refine the
separation of gradient spaces between ID and OOD samples. We quantify OOD
detection efficacy using the distance to the nearest neighbor gradients derived
from the training set, yielding a robust OOD score. Experimental evaluations
substantiate that the introduction of targeted input mix-upamplifies the
separation between ID and OOD in the gradient space, yielding impressive
results across diverse datasets. Notably, when benchmarked against ImageNet-1k,
GROOD surpasses the established robustness of state-of-the-art baselines.
Through this work, we establish the utility of leveraging gradient spaces and
class prototypes for enhanced OOD detection for DNN in image classification.

使用渐变空间和类原型增强深度神经网络（DNN）在图像分类中的离群样本检测

GROOD：插值流形中的梯度感知外部分布检测

GROOD: GRadient-aware Out-Of-Distribution detection in interpolated  manifolds

Detecting out-of-distribution (OOD) data has become a critical component in
ensuring the safe deployment of machine learning models in the real world.
Existing OOD detection approaches primarily rely on the output or feature space
for deriving OOD scores, while largely overlooking information from the
gradient space. In this paper, we present GradNorm, a simple and effective
approach for detecting OOD inputs by utilizing information extracted from the
gradient space. GradNorm directly employs the vector norm of gradients,
backpropagated from the KL divergence between the softmax output and a uniform
probability distribution. Our key idea is that the magnitude of gradients is
higher for in-distribution (ID) data than that for OOD data, making it
informative for OOD detection. GradNorm demonstrates superior performance,
reducing the average FPR95 by up to 16.33% compared to the previous best
method.

本文提出了一种名为 GradNorm 的基于梯度空间的方法，通过利用从梯度空间提取的信息来检测异常数据，可以降低平均误警率（FPR95）高达 16.33％，相对于现有最佳方法效果更好。

探测野外分布移变的梯度重要性

On the Importance of Gradients for Detecting Distributional Shifts in  the Wild

Heterogeneity across clients in federated learning (FL) usually hinders the
optimization convergence and generalization performance when the aggregation of
clients' knowledge occurs in the gradient space. For example, clients may
differ in terms of data distribution, network latency, input/output space,
and/or model architecture, which can easily lead to the misalignment of their
local gradients. To improve the tolerance to heterogeneity, we propose a novel
federated prototype learning (FedProto) framework in which the clients and
server communicate the abstract class prototypes instead of the gradients.
FedProto aggregates the local prototypes collected from different clients, and
then sends the global prototypes back to all clients to regularize the training
of local models. The training on each client aims to minimize the
classification error on the local data while keeping the resulting local
prototypes sufficiently close to the corresponding global ones. Moreover, we
provide a theoretical analysis to the convergence rate of FedProto under
non-convex objectives. In experiments, we propose a benchmark setting tailored
for heterogeneous FL, with FedProto outperforming several recent FL approaches
on multiple datasets.

该论文提出了一种新的联邦原型学习 (FedProto) 框架，借助全局原型而不是梯度来弥补跨客户端的异质性，从而提高局部模型的性能。

FedProto: 跨异构客户端的联邦原型学习

FedProto: Federated Prototype Learning across Heterogeneous Clients

Differentially private SGD (DP-SGD) is one of the most popular methods for
solving differentially private empirical risk minimization (ERM). Due to its
noisy perturbation on each gradient update, the error rate of DP-SGD scales
with the ambient dimension $p$, the number of parameters in the model. Such
dependence can be problematic for over-parameterized models where $p \gg n$,
the number of training samples. Existing lower bounds on private ERM show that
such dependence on $p$ is inevitable in the worst case. In this paper, we
circumvent the dependence on the ambient dimension by leveraging a
low-dimensional structure of gradient space in deep networks -- that is, the
stochastic gradients for deep nets usually stay in a low dimensional subspace
in the training process. We propose Projected DP-SGD that performs noise
reduction by projecting the noisy gradients to a low-dimensional subspace,
which is given by the top gradient eigenspace on a small public dataset. We
provide a general sample complexity analysis on the public dataset for the
gradient subspace identification problem and demonstrate that under certain
low-dimensional assumptions the public sample complexity only grows
logarithmically in $p$. Finally, we provide a theoretical analysis and
empirical evaluations to show that our method can substantially improve the
accuracy of DP-SGD in the high privacy regime (corresponding to low privacy
loss $\epsilon$).

本文提出了一种投影 DP-SGD 方法，通过将噪声梯度投影到低维子空间来减少噪音，并在小型公共数据集中通过一般样本复杂度分析来确定此子空间。实证研究表明，该方法可以显著提高 DP-SGD 的准确性，特别是在高隐私损失情况下。