Toward Robust Hierarchical Federated Learning in Internet of Vehicles

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations



Original languageEnglish
Pages (from-to)5600-5614
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Issue number5
Online published16 Feb 2023
Publication statusPublished - May 2023


The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large volumes of distributed data at vehicles, which can be harnessed to build models for intelligent applications. Federated learning has recently received wide attentions, which allows model training over distributed datasets without requiring raw datasets to be shared out. However, federated learning is known to be vulnerable to poisoning attacks, where malicious clients may manipulate the local datasets or model updates to corrupt the global model. Such attacks have to be countered when federated learning is adopted in IoV systems, given that the training process is distributed among a large number of vehicles in an open environment. In addition, IoV systems present a hierarchical architecture in practice where other types of nodes sit between the cloud server and vehicles, allowing intermediate aggregation for reducing overall training latency. Yet the intermediate aggregation nodes may also pose threats. In this paper, we propose a robust hierarchical federated learning framework named RoHFL, which allows hierarchical federated learning to be suitably applied in the IoV with robustness against poisoning attacks. We develop a robust model aggregation scheme that contains a logarithm-based normalization mechanism to cope with scaled gradients from malicious vehicles. We integrate the notion of reputation into the aggregation process and develop a scheme for reputation updating. We provide a formal analysis of RoHFL's convergence guarantees. Experiment results over several popular datasets demonstrate the promising performance of RoHFL, which is superior to prior work in the robustness against poisoning attacks. © 2023 IEEE.

Research Area(s)

  • Federated learning, Training, Servers, Internet of Vehicles, Convergence, Computational modeling, hierarchical federated learning, poisoning attacks, robustness, MODEL AGGREGATION, EDGE