FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
This addresses efficiency and accuracy issues in federated learning for resource-constrained IoV networks, but it is incremental as it builds on existing methods like FedAvg.
The paper tackles the challenge of participant selection in federated learning for heterogeneous Internet of Vehicles networks by proposing FedCLF, which uses calibrated loss and feedback control to improve model accuracy and resource efficiency, achieving up to a 16% improvement in accuracy over baselines in high heterogeneity scenarios.
Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications, in particular, the Internet of Vehicles (IoV) networks. However, FL encounters considerable challenges in such networks owing to the high data and device heterogeneity. To address these challenges, we propose FedCLF, i.e., FL with Calibrated Loss and Feedback control, which introduces calibrated loss as a utility in the participant selection process and a feedback control mechanism to dynamically adjust the sampling frequency of the clients. The envisaged approach (a) enhances the overall model accuracy in case of highly heterogeneous data and (b) optimizes the resource utilization for resource constrained IoV networks, thereby leading to increased efficiency in the FL process. We evaluated FedCLF vis-à-vis baseline models, i.e., FedAvg, Newt, and Oort, using CIFAR-10 dataset with varying data heterogeneity. Our results depict that FedCLF significantly outperforms the baseline models by up to a 16% improvement in high data heterogeneity-related scenarios with improved efficiency via reduced sampling frequency.