FORLA:Federated Object-centric Representation Learning with Slot Attention

• URL: http://arxiv.org/abs/2506.02964v1
• Date: Tue, 03 Jun 2025 14:59:22 GMT
• Title: FORLA:Federated Object-centric Representation Learning with Slot Attention
• Authors: Guiqiu Liao, Matjaz Jogan, Eric Eaton, Daniel A. Hashimoto,
• Abstract summary: FORLA is a novel framework for federated object-centric representation learning and feature adaptation.<n>Students learn to reconstruct full features from foundation models, while teachers reconstruct their adapted, low-dimensional counterpart.<n>Experiments in multiple real-world datasets show that FORLA learns a compact, universal representation that generalizes well across domains.
• Score: 11.037091276465734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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