Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably incr...Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.展开更多
The development of the Internet of Things has led to a significant increase in the number of devices,consequently generating a vast amount of data and resulting in an influx of unlabeled data.Collecting these data ena...The development of the Internet of Things has led to a significant increase in the number of devices,consequently generating a vast amount of data and resulting in an influx of unlabeled data.Collecting these data enables the training of robust models to support a broader range of applications.However,labeling these data can be costly,and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things,where new data continuously emerge.To address this challenge,Self-Supervised Learning(SSL)offers a way to train models without the need for labels.Nevertheless,the data stored locally in vehicles are considered private,and vehicles are reluctant to share data with others.Federated Learning(FL)is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously.Additionally,vehicles capture images while driving through cameras mounted on their rooftops.If a vehicle’s velocity is too high,the captured images,donated as local data,may be blurred.Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL.This paper proposes a FL algorithm for aggregation based on image blur levels,which is called FLSimCo.This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks.Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.展开更多
文摘Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.
基金supported in part by the National Natural Science Foundation of China under Grant No.61701197in part by the National Key Research and Development Program of China under Grant No.2021YFA1000500(4)in part by the 111 Project under Grant No.B23008.
文摘The development of the Internet of Things has led to a significant increase in the number of devices,consequently generating a vast amount of data and resulting in an influx of unlabeled data.Collecting these data enables the training of robust models to support a broader range of applications.However,labeling these data can be costly,and the models dependent on labeled data are often unsuitable for rapidly evolving fields like vehicular networks and mobile Internet of Things,where new data continuously emerge.To address this challenge,Self-Supervised Learning(SSL)offers a way to train models without the need for labels.Nevertheless,the data stored locally in vehicles are considered private,and vehicles are reluctant to share data with others.Federated Learning(FL)is an advanced distributed machine learning approach that protects each vehicle’s privacy by allowing models to be trained locally and the model parameters to be exchanged across multiple devices simultaneously.Additionally,vehicles capture images while driving through cameras mounted on their rooftops.If a vehicle’s velocity is too high,the captured images,donated as local data,may be blurred.Simple aggregation of such data can negatively impact the accuracy of the aggregated model and slow down the convergence speed of FL.This paper proposes a FL algorithm for aggregation based on image blur levels,which is called FLSimCo.This algorithm does not require labels and serves as a pre-training stage for SSL in vehicular networks.Simulation results demonstrate that the proposed algorithm achieves fast and stable convergence.
