RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence

作     者：Yang, Changan Chen, Yaxing Zhang, Yao Cui, Helei Yu, Zhiwen Guo, Bin Yan, Zheng Yang, Zijiang 

作者机构：School of Software Northwestern Polytechnical University Xi’an China School of Computer Science Northwestern Polytechnical University Xi’an China School of Cyber Engineering Xidian University Xi’an China Turing Research Center for Interdisciplinary Information Science Xi'an Jiaotong University Xi’an China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Vehicular ad hoc networks 

摘      要：Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial. First, the centralized model aggregation is unreliable in VCI because of the existence of stragglers with unfavorable channel conditions. Second, existing FL schemes are vulnerable to Non-IID data, which is intensified by the data heterogeneity in VCI. This paper proposes a novel federated learning framework called RaftFed to facilitate privacy-preserving VCI. The experimental results show that RaftFed performs better than baselines regarding communication overhead, model accuracy, and model convergence. Copyright © 2023, The Authors. All rights reserved.