FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution

• URL: http://arxiv.org/abs/2503.13745v1
• Date: Mon, 17 Mar 2025 22:05:24 GMT
• Title: FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution
• Abstract summary: Video Super-Resolution (VSR) reconstructs high-resolution videos from low-resolution inputs to restore fine details and improve visual clarity.<n>Deep learning-based VSR methods achieve impressive results, but their centralized nature raises serious privacy concerns.<n>We propose FedVSR1, a novel, architecture-independent, and stateless FL framework for VSR.
• Score: 1.3980986259786223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Super-Resolution (VSR) reconstructs high-resolution videos from low-resolution inputs to restore fine details and improve visual clarity. While deep learning-based VSR methods achieve impressive results, their centralized nature raises serious privacy concerns, particularly in applications with strict privacy requirements. Federated Learning (FL) offers an alternative approach, but existing FL methods struggle with low-level vision tasks, leading to suboptimal reconstructions. To address this, we propose FedVSR1, a novel, architecture-independent, and stateless FL framework for VSR. Our approach introduces a lightweight loss term that improves local optimization and guides global aggregation with minimal computational overhead. To the best of our knowledge, this is the first attempt at federated VSR. Extensive experiments show that FedVSR outperforms general FL methods by an average of 0.85 dB in PSNR, highlighting its effectiveness. The code is available at: https://github.com/alimd94/FedVSR

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