The rise of Artificial Intelligence (AI) has revolutionized numerous industries and transformed the way society operates. Its widespread use has led to the distribution of AI and its underlying data across many intelligent systems. In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities. As a result, modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes. In line with this goal, the latest AI models are frequently trained in a decentralized manner. Distributed learning involves multiple entities working together to make collective predictions and decisions. However, this collaboration can also bring about security vulnerabilities and challenges. This paper provides an in-depth survey on private knowledge sharing in distributed learning, examining various knowledge components utilized in leading distributed learning architectures. Our analysis sheds light on the most critical vulnerabilities that may arise when using these components in a distributed setting. We further identify and examine defensive strategies for preserving the privacy of these knowledge components and preventing malicious parties from manipulating or accessing the knowledge information. Finally, we highlight several key limitations of knowledge sharing in distributed learning and explore potential avenues for future research.

翻译：人工智能（AI）的崛起彻底改变了众多行业，并重塑了社会运作方式。其广泛应用导致AI及其底层数据分布于众多智能系统之中。因此，在分布式或由不同实体拥有的学习过程中利用信息变得至关重要。为此，现代数据驱动服务已被开发出来，将分布式知识实体整合到其成果中。基于这一目标，最新的AI模型常以去中心化方式进行训练。分布式学习涉及多个实体协同工作，以实现集体预测与决策。然而，这种协作也可能引发安全漏洞与挑战。本文对分布式学习中的私有知识共享进行了深入综述，审视了主流分布式学习架构中使用的各类知识组件。我们的分析揭示了在分布式环境中使用这些组件时可能出现的最关键漏洞。我们进一步识别并探讨了保护这些知识组件隐私、防止恶意方操纵或获取知识信息的防御策略。最后，我们强调了分布式学习中知识共享的若干关键局限性，并探索了未来研究的潜在方向。