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SoK: Private Knowledge Sharing in Distributed Learning

Year: 2025

Type: article

Abstract: The rapid advancement of Artificial Intelligence (AI) has transformed various industries, leading to the widespread distribution of AI models and data across intelligent systems. As modern data driven services increasingly integrate distributed knowledge entities, decentralized learning has become a prevalent approach to training AI models. However, this collaborative learning paradigm introduces significant security vulnerabilities and privacy challenges. This paper presents a comprehensive systematic review on private knowledge sharing in distributed learning, analyzing key knowledge components utilized in leading distributed learning architectures. We identify critical vulnerabilities associated with these components and examine defensive strategies to safeguard privacy while mitigating potential adversarial threats. Additionally, we highlight key limitations in knowledge sharing in distributed learning and propose future research directions to enhance security and efficiency in decentralized AI systems.

Source: Proceedings on Privacy Enhancing Technologies

DOI: 10.56553/popets-2025-0141

Authors: Yasas Supeksala, Thilina Ranbaduge, Ming Ding, Dinh C. Nguyen, Bo Liu, Caslon Chua, Jun Zhang

Institutions: Swinburne University of Technology, Commonwealth Scientific and Industrial Research Organisation, Data61, University of Alabama, University of Technology Sydney

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4412373460

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