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

17 March 2025
Ali Mollaahmadi Dehaghi
Hossein KhademSohi
Reza Razavi
Steve Drew
Mohammad Moshirpour
    FedML
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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:this https URL

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@article{dehaghi2025_2503.13745,
  title={ FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution },
  author={ Ali Mollaahmadi Dehaghi and Hossein KhademSohi and Reza Razavi and Steve Drew and Mohammad Moshirpour },
  journal={arXiv preprint arXiv:2503.13745},
  year={ 2025 }
}
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