基于自适应特征聚合与知识迁移的个性化联邦学习
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer
AI总结：
本文提出FedAFK方法，通过自适应特征聚合与知识迁移平衡泛化与个性化，在Non-IID数据上提升个性化联邦学习性能，并在多数据集实验中优于13个基线。
摘要：
联邦学习（FL）作为一种隐私保护的机器学习范式，能够在分散数据上训练单一模型，因而受到广泛关注。然而，统计异质性对FL构成了重大挑战。作为FL的一个子领域，个性化联邦学习（pFL）因其能够在非独立同分布（Non-IID）数据上取得良好性能的个性化模型而备受关注。但现有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数据个性化方面存在局限。为解决这一问题，我们提出了一种新方法——基于自适应特征聚合与知识迁移的个性化联邦学习（FedAFK），旨在训练更好的特征提取器，同时为参与客户端平衡泛化与个性化，从而提升个性化模型在Non-IID数据上的性能。我们在两种广泛使用的异构设置下，对三个数据集进行了大量实验，结果表明所提方法优于十三个最先进的基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
