去中心化联邦学习框架，实现多源域适应与隐私保护
DeFed-GMM-DaDiL: A Decentralized Federated Framework for Domain Adaptation
- 各客户端用高斯混合模型建模数据，通过共享可学习原子联合优化
- 在目标域缺类情况下仍保持稳定表示，能有效重建缺失类别
- 无需中央服务器，适合数据隐私要求高的多源域适应场景
去中心化多源域适应旨在将多个异构且相关的源域知识迁移到无标签的目标域，且不依赖中心服务器。本文提出去中心化联邦框架 DeFed-GMM-DaDiL，基于 GMM-数据字典学习（DaDiL）框架扩展而来。每个客户端将其数据集建模为高斯混合模型（GMM），联邦通过共享的、可学习的 GMM 原子的有标签沃尔德斯坦平均来联合逼近这些模型。该设计在无需中心服务器的前提下实现知识迁移并保护客户端隐私。我们在目标域存在缺失类别的情况下实证研究了所学表示的稳定性。实验表明，DeFed-GMM-DaDiL 在客户端间保持稳定一致的共享表示，能有效重建缺失类别，并在多源域适应基准上取得具有竞争力的性能。
原文摘要 · Abstract (English)
Decentralized multi-source domain adaptation seeks to transfer knowledge from multiple heterogeneous and related source domains to an unlabeled target domain in a decentralized setting. We address this challenge through a fully decentralized federated approach, DeFed-GMM-DaDiL, an extension of the GMM-Dataset Dictionary Learning (DaDiL) framework. Each client models its dataset as a Gaussian Mixture Model (GMM), and the federation jointly approximates them via labeled Wasserstein barycenters of shared, learnable GMM atoms. This design enables adaptation without a central server while preserving clients' privacy. We empirically study the stability of the learned representations in scenarios where the target domain has missing classes. Empirical results demonstrate that DeFed-GMM-DaDiL maintains stable and consistent shared representations across clients, effectively reconstructs missing classes, and achieves competitive performance on multi-source domain adaptation benchmarks.
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