提出新方法提升个性化联邦学习在非独立同分布数据上的表现
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer
- 自适应特征聚合与知识迁移结合，平衡全局泛化与本地个性化
- 在三个数据集上优于13个主流基线方法，显著提升模型性能
- 适合处理数据异构性强的分布式学习场景，如医疗、金融
联邦学习（FL）作为一种保护隐私的机器学习范式，在分散数据上训练统一模型方面广受欢迎。然而，统计异质性给联邦学习带来重大挑战。作为其子领域，个性化联邦学习（pFL）因其能在非独立同分布（Non-IID）数据上实现个性化模型而受到关注。然而，现有pFL方法在利用全局模型知识增强泛化能力的同时实现本地个性化方面存在局限。为此，我们提出一种新方法——个性化联邦学习自适应特征聚合与知识迁移（FedAFK），在训练更优特征提取器的同时，平衡各参与客户端的泛化与个性化性能，从而提升Non-IID数据上个性化模型的表现。我们在两个常用异构设置下的三个数据集上进行了广泛实验，结果表明所提方法优于十三种先进基线。
原文摘要 · Abstract (English)
Federated Learning(FL) is popular as a privacy-preserving machine learning paradigm for generating a single model on decentralized data. However, statistical heterogeneity poses a significant challenge for FL. As a subfield of FL, personalized FL (pFL) has attracted attention for its ability to achieve personalized models that perform well on non-independent and identically distributed (Non-IID) data. However, existing pFL methods are limited in terms of leveraging the global model's knowledge to enhance generalization while achieving personalization on local data. To address this, we proposed a new method personalized Federated learning with Adaptive Feature Aggregation and Knowledge Transfer (FedAFK), to train better feature extractors while balancing generalization and personalization for participating clients, which improves the performance of personalized models on Non-IID data. We conduct extensive experiments on three datasets in two widely-used heterogeneous settings and show the superior performance of our proposed method over thirteen state-of-the-art baselines.
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