2023 IEEE Symposium on Computers and Communications (ISCC)最新文献_第5页

Mobile LoRa Gateway for Communication and Sensing on the Railway 用于铁路通信和传感的移动LoRa网关

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218029

J. Soares, Miguel Luís, S. Sargento

引用次数: 0

AlgoID: A Blockchain Reliant Self-Sovereign Identity Framework on Algorand AlgoID:基于Algorand的区块链依赖自我主权身份框架

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218198

Andrea De Salve, D. Maesa, Fabio Federico, P. Mori, L. Ricci

引用次数: 0

Prediction of RTT Through Radio-Layer Parameters in 4G/5G Dual-Connectivity Mobile Networks 基于无线层参数的4G/5G双连接移动网络RTT预测

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218091

S. Zinno, Antonia Affinito, N. Pasquino, G. Ventre, A. Botta

引用次数: 0

A Cognitive Module for Secondary Radios Operating at the 2.5 GHz LTE Band on Indoor Environments 室内环境下2.5 GHz LTE频段二次无线电的认知模块

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218298

Marilson Duarte Soares, D. Passos, P. Castellanos

引用次数: 0

Accelerating Block Propagation with Sender Switchover in a Blockchain 通过区块链中的发送者切换加速块传播

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/iscc58397.2023.10217880

Akira Sakurai, Kazuyuki Shudo

引用次数: 0

Contrastive Learning with Attention Mechanism and Multi-Scale Sample Network for Unpaired Image-to-Image Translation 基于注意机制和多尺度样本网络的非配对图像到图像翻译的对比学习

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218053

Yunhao Liu, Songyi Zhong, Zhenglin Li, Yangqiaoyu Zhou

引用次数: 0

FedEF: Federated Learning for Heterogeneous and Class Imbalance Data 异构和类不平衡数据的联邦学习

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218040

Hong Peng, Tongtong Wu, Zhenkui Shi, Xianxian Li

{"title":"FedEF: Federated Learning for Heterogeneous and Class Imbalance Data","authors":"Hong Peng, Tongtong Wu, Zhenkui Shi, Xianxian Li","doi":"10.1109/ISCC58397.2023.10218040","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10218040","url":null,"abstract":"Federated learning (FL) is a scheme that enables multiple participants to cooperate to train a high-performance machine learning model in a way that data cannot be exported. FL effectively protects the data privacy of all participants and reduces communication costs. However, a key challenge for federated learning is the data heterogeneity across clients. In addition, in real FL applications, the class distribution of data is usually unbalanced. Although many researches have been conducted to solve the problem of data heterogeneity, class imbalance problem usually arises along with the heterogeneity data, resulting in the poor performance of the global model. In this paper, a novel FL method (we call it FedEF) is designed for heterogeneous data and local class imbalance problem via optimize feature extractors and classifiers. FedEF optimizes the local feature extractor representation of individual clients through contrastive learning to maximize the consistency of the feature extractor representation trained by the local client and the central server to handle heterogeneous data. Meanwhile, we modified the cross entropy loss in the model, assigned different loss weights to different classes of data, paid more attention to the class with fewer samples in the training process, and corrected the biased classifier to alleviate the problem of class imbalance, thus can improve the performance of the global model. Experiments show that FedEF is an effective solution to FL model obtained under heterogeneous and local class imbalance.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115124162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of a Collision Prediction System for VRUs Using V2X and Machine Learning: Intersection Collision Avoidance for Motorcycles 基于V2X和机器学习的vru碰撞预测系统评估:摩托车的交叉碰撞避免

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10218254

B. Ribeiro, Alexandre J. T. Santos, M. J. Nicolau

引用次数: 0

Cross-Regional Task Offloading with Multi-Agent Reinforcement Learning for Hierarchical Vehicular Fog Computing 基于多智能体强化学习的分层车辆雾计算跨区域任务卸载

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10217881

Yukai Hou, Zhiwei Wei, Shiyang Liu, Bing Li, Rongqing Zhang, Xiang Cheng, Liuqing Yang

{"title":"Cross-Regional Task Offloading with Multi-Agent Reinforcement Learning for Hierarchical Vehicular Fog Computing","authors":"Yukai Hou, Zhiwei Wei, Shiyang Liu, Bing Li, Rongqing Zhang, Xiang Cheng, Liuqing Yang","doi":"10.1109/ISCC58397.2023.10217881","DOIUrl":"https://doi.org/10.1109/ISCC58397.2023.10217881","url":null,"abstract":"Vehicular fog computing (VFC) can make full use of computing resources of idle vehicles to increase computing capability. However, most current VFC architectures only focus on the local region and ignore the spatio-temporal distribution of computing resources, resulting that some regions have idle computing resources while others cannot satisfy the requirements of tasks. Therefore, we propose a hierarchical VFC architecture, where neighboring regions can share their idle computing resources. Considering that the existing centralized offloading mode is not scalable enough and the high complexity of cooperative task offloading, we put forward a distributed task offloading strategy based on multi-agent reinforcement learning. Moreover, to tackle the inefficiency caused by the multi-agent credit assignment problem, we provide the counterfactual multi-agent reinforcement learning approach which exploits a counterfactual baseline to evaluate the action of each agent. Simulation results validate that the hierarchical architecture and the distributed algorithm improves the efficiency of global performance.","PeriodicalId":265337,"journal":{"name":"2023 IEEE Symposium on Computers and Communications (ISCC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124962135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedCrowdSensing: Incentive Mechanism for Crowdsensing Based on Reputation and Federated Learning 基于声誉和联邦学习的众筹激励机制

2023 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2023-07-09 DOI: 10.1109/ISCC58397.2023.10217955

Jian-quan Ouyang, Wenke Wang

引用次数: 0