QFedPolyp: A Communication- and Inference-Efficient Federated Learning Framework for Polyp Segmentation

AI 中文解读

QFedPolyp来了！这套新框架专为医疗影像中的肠道息肉分割设计，最亮眼的是它在保护病人隐私的同时，把医院之间传数据的通信成本砍掉了四分之三，模型推理速度还快了1.5倍。简单说，以前各家医院要训练AI帮忙识别息肉，要么把私密的病人数据集中到一块（风险大），要么用联邦学习但每次都要传大量参数（太慢太贵）。QFedPolyp的做法是让每家医院在自己电脑上训练一个轻量级AI模型，训练时就模拟“压缩”过程，之后只传压缩后的参数到中央服务器聚合，就像用模糊照片替代高清原图来沟通，效果却几乎一样。测试下来，压缩后的模型在多个公开数据集上依然能准确分割息肉。这意味着未来医院之间可以安全、低成本地协作开发更聪明的诊断工具，医生使用这种轻量模型做实时分析时等待时间更短，早期结直肠癌的筛查效率有望大幅提升，最终惠及每位患者。

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 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.