TY  - JOUR
AB  - 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.
AU  - Supeksala, Y
AU  - Ranbaduge, T
AU  - Ding, M
AU  - Nguyen, DC
AU  - Liu, B
AU  - Chua, C
AU  - Zhang, J
DO  - 10.56553/popets-2025-0141
EP  - 506
JO  - Proceedings on Privacy Enhancing Technologies
PB  - Privacy Enhancing Technologies Symposium Advisory Board
SP  - 485
TI  - SoK: Private Knowledge Sharing in Distributed Learning
VL  - 2025
Y2  - 2026/06/07
ER  -