The emergence of vertical federated learning (VFL) has stimulated concerns
about the imperfection in privacy protection, as shared feature embeddings may
reveal sensitive information under privacy attacks. This paper studies the
delicate equilibrium between data privacy and task utility goals of VFL under
differential privacy (DP). To address the generality issue of prior arts, this
paper advocates a flexible and generic approach that decouples the two goals
and addresses them successively. Specifically, we initially derive a rigorous
privacy guarantee by applying norm clipping on shared feature embeddings, which
is applicable across various datasets and models. Subsequently, we demonstrate
that task utility can be optimized via adaptive adjustments on the scale and
distribution of feature embeddings in an accuracy-appreciative way, without
compromising established DP mechanisms. We concretize our observation into the
proposed VFL-AFE framework, which exhibits effectiveness against privacy
attacks and the capacity to retain favorable task utility, as substantiated by
extensive experiments.

本文研究了垂直联邦学习在数据隐私和任务效用目标之间的微妙平衡，并提出了一种灵活而通用的方法来解决这个问题。通过对共享特征嵌入进行规范剪裁，我们首先得出了严格的隐私保证，接下来通过对特征嵌入的规模和分布进行自适应调整，我们还优化了任务效用，同时不损害建立的差分隐私机制。通过大量实验证明了我们提出的 VFL-AFE 框架能有效抵御隐私攻击并保持有利的任务效用。

具有自适应特征嵌入的灵活差分隐私垂直联邦学习

Flexible Differentially Private Vertical Federated Learning with  Adaptive Feature Embeddings

In this paper, we explore an approach to auxiliary task discovery in
reinforcement learning based on ideas from representation learning. Auxiliary
tasks tend to improve data efficiency by forcing the agent to learn auxiliary
prediction and control objectives in addition to the main task of maximizing
reward, and thus producing better representations. Typically these tasks are
designed by people. Meta-learning offers a promising avenue for automatic task
discovery; however, these methods are computationally expensive and challenging
to tune in practice. In this paper, we explore a complementary approach to the
auxiliary task discovery: continually generating new auxiliary tasks and
preserving only those with high utility. We also introduce a new measure of
auxiliary tasks usefulness based on how useful the features induced by them are
for the main task. Our discovery algorithm significantly outperforms random
tasks, hand-designed tasks, and learning without auxiliary tasks across a suite
of environments.

本文介绍了一种基于表示学习思想的强化学习辅助任务发现方法，通过不断生成新的辅助任务并保留具有较高效用性的任务来提高数据效率，并引入了一种反映辅助任务效用的衡量标准。实现的算法在多种环境下显著优于随机任务和手动设计的任务。