A Parameter Decoupling Approach in Personalized Federated Learning for Gastric Cancer Pathology Classification

Existing personalized federated learning methods face challenges when processing complex microscopic features, such as cellular morphology and tissue structure, in pathological images, often encountering difficulty in accurately capturing critical pathological features in participants' local models. The parameter decoupling-based personalized federated learning method can effectively address the aforementioned issues. However, different parameter decoupling settings can impact the model's ability to extract features from pathological images. This paper proposes a parameter decoupling strategy that divides the model into two core components: the classifier layer and the connecting layer. The connecting layer focuses on capturing and transmitting key information, ensuring effective feature transfer across different levels; the classifier layer is responsible for integrating features and generating the final task output, ensuring efficient aggregation of information during the decision-making process. Through this parameter decoupling strategy, information can flow efficiently across different levels, enabling the model to extract key information related to cellular lesions more effectively. Experimental results demonstrate that the proposed method outperforms other personalized federated learning approaches in the task of gastric cancer pathology image classification, achieving an accuracy improvement of % over the baseline, validating the effectiveness of the proposed framework and its corresponding parameter decoupling strategy.