Estimation of conditional average treatment effects (CATEs) is an important
topic in various fields such as medical and social sciences. CATEs can be
estimated with high accuracy if distributed data across multiple parties can be
centralized. However, it is difficult to aggregate such data if they contain
privacy information. To address this issue, we proposed data collaboration
double machine learning (DC-DML), a method that can estimate CATE models with
privacy preservation of distributed data, and evaluated the method through
numerical experiments. Our contributions are summarized in the following three
points. First, our method enables estimation and testing of semi-parametric
CATE models without iterative communication on distributed data.
Semi-parametric or non-parametric CATE models enable estimation and testing
that is more robust to model mis-specification than parametric models. However,
to our knowledge, no communication-efficient method has been proposed for
estimating and testing semi-parametric or non-parametric CATE models on
distributed data. Second, our method enables collaborative estimation between
different parties as well as multiple time points because the
dimensionality-reduced intermediate representations can be accumulated. Third,
our method performed as well or better than other methods in evaluation
experiments using synthetic, semi-synthetic and real-world datasets.

我们提出了一种名为数据协作双重机器学习（DC-DML）的方法，用于估计具有隐私保护的分布式数据的 CATE 模型，并通过数值实验对该方法进行了评估。

分布式数据上的条件平均处理效应估计：一种保护隐私的方法

Estimation of conditional average treatment effects on distributed data:  A privacy-preserving approach

Organizations are increasingly recognizing the value of data collaboration
for data analytics purposes. Yet, stringent data protection laws prohibit the
direct exchange of raw data. To facilitate data collaboration, federated
Learning (FL) emerges as a viable solution, which enables multiple clients to
collaboratively train a machine learning (ML) model under the supervision of a
central server while ensuring the confidentiality of their raw data. However,
existing studies have unveiled two main risks: (i) the potential for the server
to infer sensitive information from the client's uploaded updates (i.e., model
gradients), compromising client input privacy, and (ii) the risk of malicious
clients uploading malformed updates to poison the FL model, compromising input
integrity. Recent works utilize secure aggregation with zero-knowledge proofs
(ZKP) to guarantee input privacy and integrity in FL. Nevertheless, they suffer
from extremely low efficiency and, thus, are impractical for real deployment.
In this paper, we propose a novel and highly efficient solution RiseFL for
secure and verifiable data collaboration, ensuring input privacy and integrity
simultaneously.Firstly, we devise a probabilistic integrity check method that
significantly reduces the cost of ZKP generation and verification. Secondly, we
design a hybrid commitment scheme to satisfy Byzantine robustness with improved
performance. Thirdly, we theoretically prove the security guarantee of the
proposed solution. Extensive experiments on synthetic and real-world datasets
suggest that our solution is effective and is highly efficient in both client
computation and communication. For instance, RiseFL is up to 28x, 53x and 164x
faster than three state-of-the-art baselines ACORN, RoFL and EIFFeL for the
client computation.

我们提出了一种新颖且高效的解决方案 RiseFL，用于安全和可验证的数据协作，同时确保输入的隐私和完整性。

低成本零知识证明的安全可验证数据协作

Secure and Verifiable Data Collaboration with Low-Cost Zero-Knowledge  Proofs

Transparency of Machine Learning models used for decision support in various
industries becomes essential for ensuring their ethical use. To that end,
feature attribution methods such as SHAP (SHapley Additive exPlanations) are
widely used to explain the predictions of black-box machine learning models to
customers and developers. However, a parallel trend has been to train machine
learning models in collaboration with other data holders without accessing
their data. Such models, trained over horizontally or vertically partitioned
data, present a challenge for explainable AI because the explaining party may
have a biased view of background data or a partial view of the feature space.
As a result, explanations obtained from different participants of distributed
machine learning might not be consistent with one another, undermining trust in
the product. This paper presents an Explainable Data Collaboration Framework
based on a model-agnostic additive feature attribution algorithm (KernelSHAP)
and Data Collaboration method of privacy-preserving distributed machine
learning. In particular, we present three algorithms for different scenarios of
explainability in Data Collaboration and verify their consistency with
experiments on open-access datasets. Our results demonstrated a significant (by
at least a factor of 1.75) decrease in feature attribution discrepancies among
the users of distributed machine learning.

为确保机器学习模型的伦理使用，该论文提出了一种基于模型不可知的可加特征归因算法（KernelSHAP）和数据协作方法的可解释数据协作框架，以应对难以解释的黑盒子模型。