AI 中文总结
研究针对息肉分割中集中式学习需共享敏感数据、联邦学习通信成本高的问题，提出QFedPolyp框架，结合量化感知训练与低精度模型通信，经实验验证其能降低通信开销、加快推理速度，适合临床实时部署。
AI 中文摘要
背景与目标：自动息肉分割有助于计算机辅助诊断和早期结直肠癌检测。集中式深度学习需要医院共享敏感医疗数据，而联邦学习虽能保护隐私，但通过反复传输全精度模型参数会带来高昂通信成本。我们提出了QFedPolyp，一种用于协作息肉分割的通信和推理高效的联邦学习框架。方法：QFedPolyp将量化感知训练与低精度模型通信相结合。各医院在私有数据上本地训练轻量级U-Net并在训练时模拟量化。客户端将量化模型参数传输到中央服务器，在那里使用联邦平均进行重构和聚合。在Kvasir-SEG、CVC-ClinicVideoDB、PolypGen和BKAI-IGH NeoPolyp上进行评估。结果：全精度联邦训练在Kvasir-SEG上的Dice分数为0.910，在CVC-ClinicVideoDB上为0.930。均匀8位通信在保持有竞争力的分割精度的同时将传输成本降低了约4倍。量化模型的推理速度也比全精度模型快1.5倍。结论：QFedPolyp能够实现隐私保护的协作息肉分割，减少通信开销并加快推理速度。由此产生的轻量级模型适用于实时临床部署。
英文摘要
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.
