CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 16 of its 57 references that are in the map. First posted on arXiv 12 May 2024.
Builds on
16 of 57 references.
References · 16
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4 more
- 2022ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Schemecited by 7
- 2022ModularFed: Leveraging Modularity in Federated Learning Frameworkscited by 4
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