面向非相同客户端类别集的对齐导航：一种标签名称锚定的联邦学习框架
Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework
AI总结：
针对客户端类别集不同甚至不重叠导致的潜在空间异构问题，提出 FedAlign 框架，以自然语言标签名和全局类别表示为锚点联合对齐标签与数据编码器。
摘要：
传统联邦分类方法，即使是那些为 non-IID 客户端设计的方法，也假设每个客户端都依据同一个通用类别集标注其本地数据。本文关注一个更一般且更具实际意义的设定，即非相同客户端类别集：客户端专注于各自不同甚至互不重叠的类别集，并寻求一个适用于这些类别并集的全局模型。如果将分类视为在数据/标签编码器产生的表示之间寻找最佳匹配，那么客户端类别集中的这种异构性会带来一个新的重大挑战——不同客户端的本地编码器可能运行在不同甚至相互独立的潜在空间中，从而使服务器端聚合变得困难。我们提出一个新颖框架 FedAlign，从标签和数据两个视角对跨客户端的潜在空间进行对齐。在标签视角，我们利用具有表达力的自然语言类别名称作为标签编码器的共同基础，以锚定类别表示并指导跨客户端的数据编码器学习。在数据视角，本地训练期间，我们将全局类别表示视为锚点，并利用与本地未知类别锚点足够接近/足够远离的数据点，来对齐跨客户端的数据编码器。我们对泛化性能的理论分析以及在四个不同任务的真实世界数据集上的大量实验证实，FedAlign 优于多种最先进的（non-IID）联邦分类方法。
英文摘要：
Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set. In this paper, we focus on a more general yet practical setting, non-identical client class sets, where clients focus on their own (different or even non-overlapping) class sets and seek a global model that works for the union of these classes. If one views classification as finding the best match between representations produced by data/label encoder, such heterogeneity in client class sets poses a new significant challenge -- local encoders at different clients may operate in different and even independent latent spaces, making it hard to aggregate at the server. We propose a novel framework, FedAlign, to align the latent spaces across clients from both label and data perspectives. From a label perspective, we leverage the expressive natural language class names as a common ground for label encoders to anchor class representations and guide the data encoder learning across clients. From a data perspective, during local training, we regard the global class representations as anchors and leverage the data points that are close/far enough to the anchors of locally-unaware classes to align the data encoders across clients. Our theoretical analysis of the generalization performance and extensive experiments on four real-world datasets of different tasks confirm that FedAlign outperforms various state-of-the-art (non-IID) federated classification methods.
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。
