QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation
Proposes QFedPolyp, a federated learning framework combining quantization-aware training with low-precision model communication for collaborative polyp segmentation. On multiple datasets, 8-bit quantized communication reduces transmission cost by about 4x while maintaining competitive segmentation accuracy and up to 1.5x faster inference, suitable for real-time clinical deployment.
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[Submitted on 23 Jul 2026]
Title:QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation
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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 preserves privacy but introduces high communication costs through repeated transmission of full-precision model parameters. We propose QFedPolyp, a communication- and inference-efficient federated learning framework for collaborative polyp segmentation.
Methods: QFedPolyp combines quantization-aware training with low-precision model communication. Each hospital locally trains a lightweight U-Net on private data while simulating quantization during training. Clients transmit quantized model parameters to a central server, where they are reconstructed and aggregated using Federated Averaging. Evaluation is performed on Kvasir-SEG, CVC-ClinicVideoDB, PolypGen, and BKAI-IGH NeoPolyp.
Results: Full-precision federated training achieves Dice scores of 0.910 on Kvasir-SEG and 0.930 on CVC-ClinicVideoDB. Uni- form 8-bit communication reduces transmission cost by approximately 4 times while preserving competitive segmentation accuracy. Quantized models also achieve up to 1.5 times faster inference than full-precision models.
Conclusions: QFedPolyp enables privacy-preserving collaborative polyp segmentation with reduced communication overhead and faster inference. The resulting lightweight models are suitable for real-time clinical deployment.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.22743 [cs.LG]
(or arXiv:2607.22743v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.22743
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Madan Baduwal [view email] [v1] Thu, 23 Jul 2026 01:50:54 UTC (12,763 KB)
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