FedCVU: Federated Learning for Cross-View Video Understanding

• URL: http://arxiv.org/abs/2603.21647v1
• Date: Mon, 23 Mar 2026 07:26:55 GMT
• Title: FedCVU: Federated Learning for Cross-View Video Understanding
• Abstract summary: Federated learning (FL) has emerged as a promising paradigm for privacy-preserving multi-camera video understanding.<n>Applying FL to cross-view scenarios faces three major challenges: (i) heterogeneous viewpoints and backgrounds lead to highly non-IID client distributions, (ii) local distribution biases cause misaligned representations, and (iii) large video architectures incur prohibitive communication overhead.<n>We propose FedCVU, a federated framework with three components: VS-Norm, CV-Align, a lightweight contrastive regularization module, and SLA, a selective layer aggregation strategy.
• Score: 9.33706986632036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has emerged as a promising paradigm for privacy-preserving multi-camera video understanding. However, applying FL to cross-view scenarios faces three major challenges: (i) heterogeneous viewpoints and backgrounds lead to highly non-IID client distributions and overfitting to view-specific patterns, (ii) local distribution biases cause misaligned representations that hinder consistent cross-view semantics, and (iii) large video architectures incur prohibitive communication overhead. To address these issues, we propose FedCVU, a federated framework with three components: VS-Norm, which preserves normalization parameters to handle view-specific statistics; CV-Align, a lightweight contrastive regularization module to improve cross-view representation alignment; and SLA, a selective layer aggregation strategy that reduces communication without sacrificing accuracy. Extensive experiments on action understanding and person re-identification tasks under a cross-view protocol demonstrate that FedCVU consistently boosts unseen-view accuracy while maintaining strong seen-view performance, outperforming state-of-the-art FL baselines and showing robustness to domain heterogeneity and communication constraints.

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