mlsanityData to 7 Oct 2026

Private Knowledge Sharing in Distributed Learning: A Survey

arXiv 2402.06682 ↗First posted 8 Feb 2024cs.CR cs.AI cs.DCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 118 references that are in the map. First posted on arXiv 8 Feb 2024.

Builds on

44 of 118 references.

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32 more
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