Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic

时间 2020-08-08

标签 differentially private asynchronous federated learning mobile edge computing urban informatic 栏目无线繁體版

原文原文链接

Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics阅读笔记文献背景及解决问题车联网中的联邦学习具体方案总结与思考文献背景及解决问题因为无线网络带宽和计算资源的限制，车辆很难使用大量数据来进行提升服务质量的机器学习，好比自动驾驶和交通预测

>>阅读原文<<

1. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
2. 论文笔记——Federated learning framework for mobile edge computing networks
3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻译)
4. Differentially Private Federated Learning: A Client Level Perspective
5. 论文笔记——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
6. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
7. Deployment Characteristics of "The Edge" in Mobile Edge Computing
8. 【论文阅读】Edge Computing Architecture for Mobile Crowdsensing
9. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
10. Edge Computing
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1. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
2. 论文笔记——Federated learning framework for mobile edge computing networks
3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻译)
4. Differentially Private Federated Learning: A Client Level Perspective
5. 论文笔记——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
6. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
7. Deployment Characteristics of "The Edge" in Mobile Edge Computing
8. 【论文阅读】Edge Computing Architecture for Mobile Crowdsensing
9. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
10. Edge Computing

>>更多相关文章<<