解决联邦学习中跨域数据异质性问题，提升模型泛化能力。
An Enhanced Federated Prototype Learning Method under Domain Shift
- 引入双层原型聚类与α-稀疏损失，增强类内相似性、降低类间相似性。
- 在Digit-5、Office-10和DomainNet上优于现有方法，提升模型鲁棒性。
- 适合存在数据分布差异的联邦学习场景，如医疗、金融等隐私敏感领域。
联邦学习（FL）可在不共享私有数据的前提下实现协作机器学习训练。大量研究表明，客户端间数据异质性，尤其是来自不同数据域时，是影响联邦学习模型性能的关键因素。本文提出一种方差感知的双层原型聚类方法，并引入新颖的α-稀疏原型损失，以增强类内相似性并降低类间相似性。为确保特征收敛至特定聚类，我们设计了改进算法FedPLCC。通过按聚类规模加权原型以增大类间距离，同时仅选择部分原型参与损失计算，以减小类内距离，避免跨域原型干扰。在Digit-5、Office-10和DomainNet数据集上的实验表明，该方法显著优于现有基准。
原文摘要 · Abstract (English)
Federated Learning (FL) allows collaborative machine learning training without sharing private data. Numerous studies have shown that one significant factor affecting the performance of federated learning models is the heterogeneity of data across different clients, especially when the data is sampled from various domains. A recent paper introduces variance-aware dual-level prototype clustering and uses a novel $α$-sparsity prototype loss, which increases intra-class similarity and reduces inter-class similarity. To ensure that the features converge within specific clusters, we introduce an improved algorithm, Federated Prototype Learning with Convergent Clusters, abbreviated as FedPLCC. To increase inter-class distances, we weight each prototype with the size of the cluster it represents. To reduce intra-class distances, considering that prototypes with larger distances might come from different domains, we select only a certain proportion of prototypes for the loss function calculation. Evaluations on the Digit-5, Office-10, and DomainNet datasets show that our method performs better than existing approaches.
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