Vertical Federated Learning (VFL) has emerged as a collaborative training
paradigm that allows participants with different features of the same group of
users to accomplish cooperative training without exposing their raw data or
model parameters. VFL has gained significant attention for its research
potential and real-world applications in recent years, but still faces
substantial challenges, such as in defending various kinds of data inference
and backdoor attacks. Moreover, most of existing VFL projects are
industry-facing and not easily used for keeping track of the current research
progress. To address this need, we present an extensible and lightweight VFL
framework VFLAIR (available at this https URL), which
supports VFL training with a variety of models, datasets and protocols, along
with standardized modules for comprehensive evaluations of attacks and defense
strategies. We also benchmark 11 attacks and 8 defenses performance under
different communication and model partition settings and draw concrete insights
and recommendations on the choice of defense strategies for different practical
VFL deployment scenario.

VFLAIR 是一个可扩展且轻量级的 VFL 框架，支持多种模型、数据集和协议的 VFL 训练，包括各种攻击和防御策略的综合评估，以及在不同通信和模型划分设置下的 11 种攻击和 8 种防御策略的性能基准研究，为不同实际 VFL 部署场景的防御策略选择提供具体见解和建议。

VFLAIR: 垂直联邦学习的研究库和基准

VFLAIR: A Research Library and Benchmark for Vertical Federated Learning

We introduce an adaptive method with formal quality guarantees for weak
supervision in a non-stationary setting. Our goal is to infer the unknown
labels of a sequence of data by using weak supervision sources that provide
independent noisy signals of the correct classification for each data point.
This setting includes crowdsourcing and programmatic weak supervision. We focus
on the non-stationary case, where the accuracy of the weak supervision sources
can drift over time, e.g., because of changes in the underlying data
distribution. Due to the drift, older data could provide misleading information
to infer the label of the current data point. Previous work relied on a priori
assumptions on the magnitude of the drift to decide how much data to use from
the past. Comparatively, our algorithm does not require any assumptions on the
drift, and it adapts based on the input. In particular, at each step, our
algorithm guarantees an estimation of the current accuracies of the weak
supervision sources over a window of past observations that minimizes a
trade-off between the error due to the variance of the estimation and the error
due to the drift. Experiments on synthetic and real-world labelers show that
our approach indeed adapts to the drift. Unlike fixed-window-size strategies,
it dynamically chooses a window size that allows it to consistently maintain
good performance.

提出了一种自适应方法，在非稳态环境下提供正式质量保证的弱监督学习，通过使用提供每个数据点的正确分类的独立嘈杂信号的弱监督学习源来推断一系列数据的未知标签，并针对准确性可能随时间漂移的非稳态情况进行了重点研究，该算法不需要先验假设并且基于输入进行自适应调整。该算法保证了在过去观察窗口内，最小化估计误差方差和漂移误差的平衡下，弱监督源的当前准确性。实验表明，与固定窗口大小策略不同，该方法可动态选择窗口大小，以保持良好性能。