Gradient inversion attacks aim to reconstruct local training data from
intermediate gradients exposed in the federated learning framework. Despite
successful attacks, all previous methods, starting from reconstructing a single
data point and then relaxing the single-image limit to batch level, are only
tested under hard label constraints. Even for single-image reconstruction, we
still lack an analysis-based algorithm to recover augmented soft labels. In
this work, we change the focus from enlarging batchsize to investigating the
hard label constraints, considering a more realistic circumstance where label
smoothing and mixup techniques are used in the training process. In particular,
we are the first to initiate a novel algorithm to simultaneously recover the
ground-truth augmented label and the input feature of the last fully-connected
layer from single-input gradients, and provide a necessary condition for any
analytical-based label recovery methods. Extensive experiments testify to the
label recovery accuracy, as well as the benefits to the following image
reconstruction. We believe soft labels in classification tasks are worth
further attention in gradient inversion attacks.

本论文研究梯度反转攻击在联邦学习框架下从中间梯度中重建本地训练数据，提出了一种基于分析的算法来同时恢复单输入梯度中的真实增强标签和最后全连接层的输入特征，并证明了该算法对标签恢复的准确性以及对随后的图像重建的好处，强调了分类任务中软标签在梯度反转攻击中值得进一步关注。

消除梯度反转攻击中的硬标签约束

Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks

This paper investigates the relationship between graph convolution and Mixup
techniques. Graph convolution in a graph neural network involves aggregating
features from neighboring samples to learn representative features for a
specific node or sample. On the other hand, Mixup is a data augmentation
technique that generates new examples by averaging features and one-hot labels
from multiple samples. One commonality between these techniques is their
utilization of information from multiple samples to derive feature
representation. This study aims to explore whether a connection exists between
these two approaches. Our investigation reveals that, under two mild
conditions, graph convolution can be viewed as a specialized form of Mixup that
is applied during both the training and testing phases. The two conditions are:
1) \textit{Homophily Relabel} - assigning the target node's label to all its
neighbors, and 2) \textit{Test-Time Mixup} - Mixup the feature during the test
time. We establish this equivalence mathematically by demonstrating that graph
convolution networks (GCN) and simplified graph convolution (SGC) can be
expressed as a form of Mixup. We also empirically verify the equivalence by
training an MLP using the two conditions to achieve comparable performance.

本研究探讨了图卷积和 Mixup 技术之间的关系。研究发现，在两个条件的基础上，图卷积可以被视为一种在训练和测试阶段都应用的特殊形式的 Mixup。这两个条件分别是：1）同质重标签 - 将目标节点的标签分配给其所有邻居；2）测试时间 Mixup - 在测试时间进行特征的混合。通过数学建模证明了图卷积网络（GCN）和简化图卷积（SGC）可以表示为 Mixup 的形式，并通过实证验证了这个等价性，使用这两个条件训练 MLP 取得了可比较的性能。

图卷积与混合操作的等价性

On the Equivalence of Graph Convolution and Mixup

Recently, federated learning has emerged as a promising approach for training
a global model using data from multiple organizations without leaking their raw
data. Nevertheless, directly applying federated learning to real-world tasks
faces two challenges: (1) heterogeneity in the data among different
organizations; and (2) data noises inside individual organizations.
In this paper, we propose a general framework to solve the above two
challenges simultaneously. Specifically, we propose using distributionally
robust optimization to mitigate the negative effects caused by data
heterogeneity paradigm to sample clients based on a learnable distribution at
each iteration. Additionally, we observe that this optimization paradigm is
easily affected by data noises inside local clients, which has a significant
performance degradation in terms of global model prediction accuracy. To solve
this problem, we propose to incorporate mixup techniques into the local
training process of federated learning. We further provide comprehensive
theoretical analysis including robustness analysis, convergence analysis, and
generalization ability. Furthermore, we conduct empirical studies across
different drug discovery tasks, such as ADMET property prediction and
drug-target affinity prediction.

本文提出了一个通用框架，通过使用分布式鲁棒优化和混合技术来解决联合学习中数据异质性和数据噪声的两个挑战，并在多个药物发现任务中进行了实证研究。