More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning

Abstract

As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users’ data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively.