通过原型增强提升边缘设备联邦学习在领域异构下的模型泛化能力
Robust Federated Learning on Edge Devices with Domain Heterogeneity
- 基于原型对比学习，用数据增强生成全局特征原型
- 在Office-10和Digits数据集上优于当前最优基线
- 适合存在领域差异的边缘智能场景应用
联邦学习（FL）可在保护数据隐私的前提下实现分布式边缘设备协同训练，是隐私敏感应用的热门方案。然而，统计异构性（尤其是领域异构性）严重阻碍了全局模型的收敛。本文提出FedAPC（联邦增强原型对比学习），一种基于原型的联邦学习框架，通过引入增强数据的均值特征生成原型，以提升特征多样性与模型鲁棒性。该方法通过将本地特征与全局原型对齐，使模型学习更具意义的语义特征，同时降低对特定领域的过拟合。在Office-10和Digits数据集上的实验表明，该框架显著优于现有最先进方法。
原文摘要 · Abstract (English)
Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices, making it a popular solution for privacy-sensitive applications. However, FL faces significant challenges due to statistical heterogeneity, particularly domain heterogeneity, which impedes the global mode's convergence. In this study, we introduce a new framework to address this challenge by improving the generalization ability of the FL global model under domain heterogeneity, using prototype augmentation. Specifically, we introduce FedAPC (Federated Augmented Prototype Contrastive Learning), a prototype-based FL framework designed to enhance feature diversity and model robustness. FedAPC leverages prototypes derived from the mean features of augmented data to capture richer representations. By aligning local features with global prototypes, we enable the model to learn meaningful semantic features while reducing overfitting to any specific domain. Experimental results on the Office-10 and Digits datasets illustrate that our framework outperforms SOTA baselines, demonstrating superior performance.
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