标题： FORLA：基于槽注意力的面向对象联邦表示学习 显示英文标题

标题： FORLA:Federated Object-centric Representation Learning with Slot Attention

摘要： 在跨异构无标注数据集的高效视觉表征学习仍然是联邦学习中的一个核心挑战。 有效的联邦表征需要特征在客户端之间具有联合信息性，同时在没有监督的情况下解耦领域特定因素。 我们引入了 FORLA，这是一种新颖的框架，用于使用无监督槽注意力在客户端之间进行联邦面向对象的表征学习和特征适应。 我们的方法的核心是一个共享的特征适配器，它通过客户端之间的协作训练来适应基础模型的特征，并且是一个共享的槽注意力模块，用于学习重建被适配的特征。 为了优化这个适配器，我们设计了一个两分支的学生-教师架构。 在每个客户端中，一个学生解码器学习从基础模型重建完整的特征，而一个教师解码器则重建它们的低维适配对应物。 共享的槽注意力模块通过对齐客户端的对象级表示来实现跨域学习。 在多个真实世界的数据集上的实验表明，我们的框架不仅在对象发现方面优于集中式基线，而且学习了一个紧凑且通用的表示，可以在领域间良好泛化。 这项工作强调了联邦槽注意力作为一种有效工具，用于从具有分布式概念的跨域数据中进行可扩展的无监督视觉表征学习。 显示英文摘要

摘要： Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative across clients while disentangling domain-specific factors without supervision. We introduce FORLA, a novel framework for federated object-centric representation learning and feature adaptation across clients using unsupervised slot attention. At the core of our method is a shared feature adapter, trained collaboratively across clients to adapt features from foundation models, and a shared slot attention module that learns to reconstruct the adapted features. To optimize this adapter, we design a two-branch student-teacher architecture. In each client, a student decoder learns to reconstruct full features from foundation models, while a teacher decoder reconstructs their adapted, low-dimensional counterpart. The shared slot attention module bridges cross-domain learning by aligning object-level representations across clients. Experiments in multiple real-world datasets show that our framework not only outperforms centralized baselines on object discovery but also learns a compact, universal representation that generalizes well across domains. This work highlights federated slot attention as an effective tool for scalable, unsupervised visual representation learning from cross-domain data with distributed concepts.