Data silos, mainly caused by privacy and interoperability, significantly
constrain collaborations among different organizations with similar data for
the same purpose. Distributed learning based on divide-and-conquer provides a
promising way to settle the data silos, but it suffers from several challenges,
including autonomy, privacy guarantees, and the necessity of collaborations.
This paper focuses on developing an adaptive distributed kernel ridge
regression (AdaDKRR) by taking autonomy in parameter selection, privacy in
communicating non-sensitive information, and the necessity of collaborations in
performance improvement into account. We provide both solid theoretical
verification and comprehensive experiments for AdaDKRR to demonstrate its
feasibility and effectiveness. Theoretically, we prove that under some mild
conditions, AdaDKRR performs similarly to running the optimal learning
algorithms on the whole data, verifying the necessity of collaborations and
showing that no other distributed learning scheme can essentially beat AdaDKRR
under the same conditions. Numerically, we test AdaDKRR on both toy simulations
and two real-world applications to show that AdaDKRR is superior to other
existing distributed learning schemes. All these results show that AdaDKRR is a
feasible scheme to defend against data silos, which are highly desired in
numerous application regions such as intelligent decision-making, pricing
forecasting, and performance prediction for products.

基于自主性参数选择、通信非敏感信息的隐私保证和性能改进的协作性的自适应分布式核岭回归 (AdaDKRR) 是解决数据孤岛问题的可行方案，并能在智能决策、价格预测和产品性能预测等领域发挥作用。

自适应分布式核岭回归：一种适用于数据孤立的可行分布式学习方案

Adaptive Distributed Kernel Ridge Regression: A Feasible Distributed  Learning Scheme for Data Silos

In recent years, Natural Language Generation (NLG) techniques in AI (e.g.,
T5, GPT-3, ChatGPT) have shown a massive improvement and are now capable of
generating human-like long coherent texts at scale, yielding so-called deepfake
texts. This advancement, despite their benefits, can also cause security and
privacy issues (e.g., plagiarism, identity obfuscation, disinformation attack).
As such, it has become critically important to develop effective, practical,
and scalable solutions to differentiate deepfake texts from human-written
texts. Toward this challenge, in this work, we investigate how factors such as
skill levels and collaborations impact how humans identify deepfake texts,
studying three research questions: (1) do collaborative teams detect deepfake
texts better than individuals? (2) do expert humans detect deepfake texts
better than non-expert humans? (3) what are the factors that maximize the
detection performance of humans? We implement these questions on two platforms:
(1) non-expert humans or asynchronous teams on Amazon Mechanical Turk (AMT) and
(2) expert humans or synchronous teams on the Upwork. By analyzing the
detection performance and the factors that affected performance, some of our
key findings are: (1) expert humans detect deepfake texts significantly better
than non-expert humans, (2) synchronous teams on the Upwork detect deepfake
texts significantly better than individuals, while asynchronous teams on the
AMT detect deepfake texts weakly better than individuals, and (3) among various
error categories, examining coherence and consistency in texts is useful in
detecting deepfake texts. In conclusion, our work could inform the design of
future tools/framework to improve collaborative human detection of deepfake
texts.

本文研究了自然语言生成技术对安全性和隐私的威胁，通过对技能水平和协作等因素的调查，研究了人类如何辨别深度伪造文本，发现专业人士及同步协作组较非专业人士及异步组在识别深度伪造文本方面表现更好，分析深度伪造文本的连贯与一致性是有用的。该工作可为未来改进深度伪造文本协作人类检测的工具 / 框架的设计提供参考。