提出去中心化联邦字典学习，实现多源域自适应
Decentralized Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation
- 用去中心化方式替代中心服务器，通过字典学习建模分布偏移
- 在无中心节点情况下仍能有效适配多个异构源域到目标域
- 适合数据隐私要求高、需分布式协作的场景
去中心化多源域适应（DMSDA）旨在在去中心化框架下，将多个相关且异构的源域知识迁移至未标注的目标域。本文提出一种完全去中心化的联邦数据字典学习方法，摒弃了中心服务器的依赖。该方法基于弗雷歇均值（Wasserstein barycenters）建模跨客户端的分布偏移，实现有效域适应的同时保障数据隐私。去中心化设计提升了系统的鲁棒性、可扩展性与隐私性，避免单点故障风险。实验对比了其与联邦版本及其他基准算法的表现，结果表明该方法可在完全去中心化环境下有效实现源域到目标域的适应。
原文摘要 · Abstract (English)
Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled target domain within a decentralized framework. Our work tackles DMSDA through a fully decentralized federated approach. In particular, we extend the Federated Dataset Dictionary Learning (FedDaDiL) framework by eliminating the necessity for a central server. FedDaDiL leverages Wasserstein barycenters to model the distributional shift across multiple clients, enabling effective adaptation while preserving data privacy. By decentralizing this framework, we enhance its robustness, scalability, and privacy, removing the risk of a single point of failure. We compare our method to its federated counterpart and other benchmark algorithms, showing that our approach effectively adapts source domains to an unlabeled target domain in a fully decentralized manner.
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