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QFedPolyp Boosts Federated Learning for Polyp Segmentation

Madan Baduwal, Priyanka Paudel· July 28, 2026 View original

Summary

This paper introduces QFedPolyp, a federated learning framework that enables privacy-preserving collaborative polyp segmentation with reduced communication overhead and faster inference. It combines quantization-aware training with low-precision model communication, allowing hospitals to train lightweight models locally and share quantized parameters.

Automatic polyp segmentation is a vital tool for computer-aided diagnosis and early detection of colorectal cancer. However, traditional centralized deep learning approaches necessitate sharing sensitive medical data among hospitals, posing significant privacy challenges. While federated learning offers a privacy-preserving alternative by allowing local model training, it often incurs high communication costs due to the repeated transmission of full-precision model parameters. Researchers have developed QFedPolyp, an innovative federated learning framework specifically designed for efficient and privacy-preserving collaborative polyp segmentation. QFedPolyp integrates quantization-aware training with low-precision model communication. Each participating hospital locally trains a lightweight U-Net model on its private data, simulating quantization during this training phase. Subsequently, only these quantized, low-precision model parameters are transmitted to a central server, where they are reconstructed and aggregated using Federated Averaging. This method significantly reduces communication costs by approximately four times while maintaining competitive segmentation accuracy. Furthermore, the resulting quantized models achieve up to 1.5 times faster inference, making them highly suitable for real-time clinical deployment.

Why it matters

This framework addresses critical challenges in medical AI by enabling privacy-preserving model training across multiple institutions while simultaneously improving efficiency in communication and inference, making advanced diagnostic tools more practical for real-world clinical use.

How to implement this in your domain

  1. 1Assess current data privacy and sharing protocols for medical imaging AI projects.
  2. 2Explore federated learning solutions like QFedPolyp for collaborative model development without centralizing sensitive data.
  3. 3Investigate integrating quantization-aware training into existing deep learning pipelines to reduce model size and inference time.
  4. 4Pilot QFedPolyp or similar frameworks with partner hospitals to collaboratively train diagnostic models while adhering to privacy regulations.
  5. 5Evaluate the trade-offs between model precision, communication efficiency, and inference speed for specific clinical applications.

Who benefits

HealthcareMedical ImagingBiotech

Key takeaways

  • Federated learning is crucial for privacy-preserving medical AI, but often has high communication costs.
  • QFedPolyp reduces communication by 4x and speeds up inference by 1.5x through quantization-aware training.
  • It maintains high accuracy for polyp segmentation, making it suitable for clinical deployment.
  • The framework enables secure, efficient collaboration among healthcare institutions for AI development.

Original post by Madan Baduwal, Priyanka Paudel

"arXiv:2607.22743v1 Announce Type: new Abstract: Background and Objective: Automatic polyp segmentation supports computer-aided diagnosis and early colorectal cancer detec- tion. Centralized deep learning requires hospitals to share sensitive medical data, while federated learning…"

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Originally posted by Madan Baduwal, Priyanka Paudel on X · view source

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