A Federated Learning Framework for Privacy-Preserving Oral Cancer Screening on Smartphones

2026-08-19

AI Summary

本文提出了一种联邦学习框架，通过使用分布式数据集和轻量级模型（如MobileNetV4-Conv-Small），在保护隐私的前提下提高了智能手机上口腔癌筛查的准确性。

Abstract

Data are the cornerstone of robust AI models. However, in the medical domain, access to reliable data is constrained by regulatory requirements and patient privacy, and clinical oral images are particularly difficult to obtain. Federated learning (FL) mitigates these constraints by enabling collaborative model development across decentralized datasets without centralizing or sharing patient data. This work presents a practical FL framework that supports geographically distributed collaboration among AI healthcare researchers and facilitates the development of robust models for oral cancer screening. Client devices were interconnected via Tailscale to provide secure networking and real-time communication. We implemented the FL workflow using the Flower framework for server-side aggregation, while client deployment and orchestration were configured manually; no enterprise FL platforms were used. To support a smartphone-based screening application, we evaluated lightweight, mobile-friendly architectures including MobileNetV2, MobileNetV3Large, and MobileNetV4-Conv-Small (MNv4-Conv-S). Across the global lightweight models aggregated using FedAvg, the MNv4-Conv-S based global model (GM-V4) achieved the best performance, reaching an AUC of 0.929 and an accuracy of 87%

Problem

• Federated Learning
• Privacy-Preserving
• Oral Cancer Screening
• Decentralized Datasets

Innovation

• Federated Learning
• Oral Cancer Screening
• Privacy Preservation
• MobileNet Variants
• Secure Networking