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Towards Multi-Party Personalized Collaborative Learning in Remote Sensing

Li, Jianzhao ; Gong, Maoguo ; Liu, Zaitian ; Wang, Shanfeng ; Zhang, Yourun ; Zhou, Yu ; Gao, Yuan

IEEE transactions on geoscience and remote sensing, 2024-02, p.1-1 [Periódico revisado por pares]

IEEE

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  • Título:
    Towards Multi-Party Personalized Collaborative Learning in Remote Sensing
  • Autor: Li, Jianzhao ; Gong, Maoguo ; Liu, Zaitian ; Wang, Shanfeng ; Zhang, Yourun ; Zhou, Yu ; Gao, Yuan
  • Assuntos: Computational modeling ; Data models ; Federated learning ; Multi-party learning (Federated learning) ; Multiprotocol label switching ; personalized collaboration ; Remote sensing ; remote sensing data security ; remote sensing image classification ; remote sensing image segmentation ; Task analysis ; Training
  • É parte de: IEEE transactions on geoscience and remote sensing, 2024-02, p.1-1
  • Descrição: The powerful deep learning models in remote sensing are inseparable from the support of massive data. However, the privacy and sensitivity of remote sensing data (RSD) restrict the possibility of each party to collaboratively train and share a large general model. Although multi-party learning (MPL) is a feasible solution, it is difficult for the existing MPL methods to uniformly process different remote sensing tasks (RSTs), and the data held by each party is non-independent and identically distributed, heterogeneous and multi-sources. Therefore, it is urgent to explore a solution for the personalized processing of different RSTs. In this paper, we formulate a novel multi-party personalized collaborative learning (MPCL) framework in terms of models and tasks. Specifically, in each iteration of the communication round, we aim to decouple personalized model optimization from global model learning. Different participants are allowed to explore their personalized local models at a certain distance from the global aggregation models according to the characteristics of their local data. In terms of task personalization, MPCL provides different personalized global models to handle the corresponding RSTs. For participants with different RSTs, it can be implemented in the multi-task collaborative training strategy to explore the connection between different tasks. To demonstrate the feasibility of MPCL, we take remote sensing image classification as a case study and provide a detailed feasibility scheme. We constructed four benchmark datasets compliant with MPL and personalized MPL, including single-source and multi-source about SAR, hyperspectral and optical RSD. The experimental results demonstrate that our MPCL is superior in these four RSD, which ranked first in the competition with the classic or state-of-the-art MPL and personalized MPL algorithms. In addition, the scalability of MPCL is also verified on image segmentation RSTs of building and road extraction.
  • Editor: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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