Federated Learning and Optimization for Few-Shot Image Classification  

作　　者：Yi Zuo Zhenping Chen Jing Feng Yunhao Fan 

机构地区：[1]School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou,215009,China

出　　处：《Computers, Materials & Continua》2025年第3期4649-4667,共19页计算机、材料和连续体(英文)

基　　金：supported by Suzhou Science and Technology Plan(Basic Research)Project under Grant SJC2023002;Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX23_3322.

摘　　要：Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical imaging.Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification,in this paper,we propose a federated learning approach that incorporates privacy-preserving techniques.First,we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size,thereby enhancing the model’s generalization capabilities in few-shot contexts.Second,we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters,perturbing the transmitted parameters to ensure user data privacy.Finally,numerical simulations are conducted to demonstrate the effectiveness of our proposed method.The results indicate that our approach significantly enhances model generalization and test accuracy compared to several popular federated learning algorithms while maintaining data privacy,highlighting its effectiveness and practicality in addressing the challenges of model generalization and data privacy in few-shot image scenarios.

关 键 词：Federated learning contrastive learning few-shot differential privacy data augmentation 

分 类 号：P20[天文地球—测绘科学与技术]