利用严肃游戏数据进行自闭症谱系障碍早期诊断的联邦异常检测
Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data
AI总结：
针对ASD早期筛查成本高、标签稀缺及游戏数据隐私风险，提出基于AutoEncoder的半监督联邦异常检测框架，并结合FHE、客户端选择和先进聚合器提升隐私保护与预测性能。
摘要：
自闭症谱系障碍（ASD）的早期识别被认为对有效干预至关重要，可减轻情感、经济和社会负担。尽管ASD属于一组无法治愈的神经发育障碍，但研究人员一致认为，儿童时期的针对性干预可显著改善个体的整体福祉。然而，传统的ASD检测方法（如筛查测试）往往成本高昂且耗时。由于获取相关任务的真实标签存在挑战，本研究提出了一种新颖的半监督ASD检测方法，采用基于AutoEncoder（自编码器）的机器学习（ML）方法。我们的方法利用通过专为该目的设计的严肃游戏手动收集的数据。由于游戏化应用收集的敏感数据易发生隐私泄露，我们开发了一个联邦学习（FL）框架，可在不损害ML模型整体性能的情况下增强用户隐私。该框架在模型聚合期间进一步通过全同态加密（FHE）加以增强，以最大限度降低推理攻击的可能性，并采用客户端选择机制以及最先进的聚合器来提高模型的预测准确率。我们的结果表明，半监督FL能够有效预测每个病例的ASD风险指标，同时解决隐私问题。
英文摘要：
Early identification of Autism Spectrum Disorder (ASD) is considered critical for effective intervention to mitigate emotional, financial and societal burdens. Although ASD belongs to a group of neurodevelopmental disabilities that are not curable, researchers agree that targeted interventions during childhood can drastically improve the overall well-being of individuals. However, conventional ASD detection methods such as screening tests, are often costly and time-consuming. This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods due to the challenge of obtaining ground truth labels for the associated task. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning (FL) framework that can enhance user privacy without compromising the overall performance of the ML models. The framework is further enhanced with Fully Homomorphic Encryption (FHE) during model aggregation to minimize the possibility of inference attacks and client selection mechanisms as well as state-of-the-art aggregators to improve the model's predictive accuracy. Our results demonstrate that semi-supervised FL can effectively predict an ASD risk indicator for each case while simultaneously addressing privacy concerns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
