提出GALA框架，解决多源联邦域适应的可扩展性难题
Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization
- 用组间差异最小化实现线性复杂度的跨域对齐
- 通过温度调控的中心点加权策略动态选择关键源域
- 适用于大量异构源域，特别适合高多样性场景
无监督多源域适应（UMDA）利用多个源域的标注数据提升对未标记目标域的泛化能力。尽管联邦学习避免了原始数据共享以保障隐私，但现有方法在源域数量增多时面临计算开销大或训练不稳定的瓶颈。本文提出GALA框架，一种可扩展且鲁棒的联邦无监督多源域适应方法，专为高多样性场景设计。GALA通过新型组间差异最小化目标，以线性复杂度近似成对对齐，并结合温度控制的中心点加权策略实现动态源域优先级分配，支持稳定且可并行的多源训练，有效解决当前文献中尚未充分关注的可扩展性瓶颈。为评估高多样性场景下的性能，我们构建了Digit-18新基准，包含18个具有不同合成与真实域偏移的数据集。大量实验表明，GALA在标准基准上达到最先进水平，在大规模设置中显著优于已有方法——后者要么无法收敛，要么计算不可行。
原文摘要 · Abstract (English)
Unsupervised multi-source domain adaptation (UMDA) leverages labeled data from multiple source domains to generalize to an unlabeled target. While federated UMDA addresses privacy by avoiding raw data sharing, existing methods scale poorly as the number of sources increases, often suffering from high computational overhead or training instability. We propose GALA, a scalable and robust federated UMDA framework designed for high-diversity settings. GALA achieves scalability by coupling a novel inter-group discrepancy minimization objective that approximates pairwise alignment with linear complexity alongside a temperature-controlled, centroid-based weighting strategy for dynamic source prioritization. These components enable stable, parallelizable training across many heterogeneous sources, addressing a critical scalability bottleneck that remains largely unaddressed in current literature. To evaluate performance in high-diversity scenarios, we introduce Digit-18, a new benchmark comprising 18 datasets with varied synthetic and real-world domain shifts. Extensive experiments demonstrate that GALA achieves state-of-the-art results on standard benchmarks and significantly outperforms prior methods in large-scale settings where others either fail to converge or become computationally infeasible.
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