Mitigating Straggler Effect in Federated Learning Based on Reconfigurable Intelligent Surface over Internet of Vehicles

To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station without massive raw data exchange.However,heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning.Dropping out vehicles with limited resources confines the training data.As a result,the accuracy and applicability of federated learning models will be reduced.To mitigate the straggler effect and improve performance of federated learning,we propose a reconfigurable intelligent surface(RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV.Furthermore,we optimize the phase shift of RIS to achieve a more reliable communication environment.In addition,we define vehicular competence to measure both vehicular trustworthiness and resources.Based on the vehicular competence,the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence.The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.

基于联邦学习的第三方库流量识别

第三方库(Third-party Library, TPL)已经成为移动应用开发的重要组成部分,开发者通常在应用中集成TPL以实现诸如广告、消息推送、移动支付等特定功能,从而提高开发效率并降低研发成本。然而,由于TPL与其所在的移动应用(宿主应用)共享相同的系统权限,且开发者对TPL自身的安全隐患缺乏了解,导致近年来由TPL引起的安全问题频发,给公众造成了严重的信息与隐私安全困扰。TPL的流量识别对于精细化流量管理与安全威胁检测具有重要意义,是支撑对宿主应用与TPL之间进行安全责任判定的重要能力,同时也是促进TPL安全合规发展的重要检测方法。然而目前关于TPL的研究主要集中于TPL检测、TPL引起的隐私泄漏问题等,关于TPL流量识别的研究十分少见。为此,本文提出并实现了一种用于TPL流量识别的框架——LibCapture,该框架首先基于动态插桩技术与TPL检测技术设计了自动生成TPL加密流量数据集的方法。其次,针对隐私保护以及数据共享的问题,构建了基于卷积神经网络的联邦学习模型,用于识别TPL流量。最后,通过对2327个真实应用的流量测试证明了本文所提框架具有较高的流量识别准确率。此外,本文分析了联邦学习参与方本地样本数据差异性给全局模型聚合带来的具体影响,指出了不同场景下的进一步研究方向。