In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) and Federated Learning (FL) have recently emerged as promising technologies for training a decentralized Machine Learning (ML) model. With the increasing use of smartphones and Internet of Things (IoT) devices, these distributed technologies enable users with limited resources to complete neural network model training with server assistance and collaboratively combine knowledge between different nodes. In this study, we propose combining these technologies to address the continuous authentication challenge while protecting user privacy and limiting device resource usage. However, the model’s training is slowed due to SL sequential training and resource differences between IoT devices with different specifications. Therefore, we use a cluster-based approach to group devices with similar capabilities to mitigate the impact of slow devices while filtering out the devices incapable of training the model. In addition, we address the efficiency and robustness of training ML models by using SL and FL techniques to train the clients simultaneously while analyzing the overhead burden of the process. Following clustering, we select the best set of clients to participate in training through a Genetic Algorithm (GA) optimized on a carefully designed list of objectives. The performance of our proposed framework is compared to baseline methods, and the advantages are demonstrated using a real-life UMDAA-02-FD face detection dataset. The results show that CRSFL, our proposed approach, maintains high accuracy and reduces the overhead burden in continuous authentication scenarios while preserving user privacy.

中文翻译：

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CRSFL：基于集群的资源感知分割联邦学习，用于持续身份验证


在不断变化的技术世界中，在用户与设备交互期间，持续身份验证和全面的访问管理至关重要。分割学习（SL）和联合学习（FL）最近成为训练去中心化机器学习（ML）模型的有前途的技术。随着智能手机和物联网（IoT）设备的使用越来越多，这些分布式技术使资源有限的用户能够在服务器协助下完成神经网络模型训练，并协作组合不同节点之间的知识。在本研究中，我们建议结合这些技术来解决持续的身份验证挑战，同时保护用户隐私并限制设备资源使用。然而，由于SL顺序训练以及不同规格物联网设备之间的资源差异，模型的训练速度变慢。因此，我们使用基于集群的方法对具有相似功能的设备进行分组，以减轻慢速设备的影响，同时过滤掉无法训练模型的设备。此外，我们通过使用 SL 和 FL 技术来同时训练客户端，同时分析流程的开销负担，从而解决训练 ML 模型的效率和鲁棒性问题。聚类之后，我们通过针对精心设计的目标列表进行优化的遗传算法 (GA) 选择最佳的一组客户来参加培训。我们提出的框架的性能与基线方法进行了比较，并使用现实生活中的 UMDAA-02-FD 人脸检测数据集证明了其优点。结果表明，我们提出的方法 CRSFL 保持了高精度，减少了连续身份验证场景中的开销负担，同时保护了用户隐私。