Time-dependent Decentralized Routing using Federated Learning
Time-dependent Decentralized Routing using Federated Learning
复制标题
使用联邦学习的时间相关的分散式路由
DOI：
10.1109/isorc49007.2020.00018
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Dubey, Abhishek
中科院分区：
文献类型：
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作者：
Wilbur, Michael;Samal, Chinmaya;Talusan, Jose Paolo;Yasumoto, Keiichi;Dubey, Abhishek
云计算的最新进展通过提供近乎实时的处理和存储可扩展性，推动了数据密集型智慧城市应用的快速发展。这催生了高效的集中式路线规划服务，例如数百万用户依赖的谷歌地图。路线规划算法的进步与其运行的云环境一致。当前最先进的解决方案采用共享内存模型，因此部署仅限于数据中心的多处理环境。通过集中这些服务，延迟已成为未来技术（例如自动驾驶汽车）的限制参数。此外，这些服务需要访问外部网络，从而引发了灾难情况下的可用性问题。因此，本文提供了一种私有雾网络的去中心化路径规划方法。我们利用联邦学习的最新进展来在线协作学习共享预测模型，并通过来自美国中型城市的模拟案例研究来研究我们的方法。
Recent advancements in cloud computing have driven rapid development in data-intensive smart city applications by providing near real time processing and storage scalability. This has resulted in efficient centralized route planning services such as Google Maps, upon which millions of users rely. Route planning algorithms have progressed in line with the cloud environments in which they run. Current state of the art solutions assume a shared memory model, hence deployment is limited to multiprocessing environments in data centers. By centralizing these services, latency has become the limiting parameter in the technologies of the future, such as autonomous cars. Additionally, these services require access to outside networks, raising availability concerns in disaster scenarios. Therefore, this paper provides a decentralized route planning approach for private fog networks. We leverage recent advances in federated learning to collaboratively learn shared prediction models online and investigate our approach with a simulated case study from a mid-size U.S. city.
DOI：
10.1007/11946441_40
发表时间：
2006
期刊：
Proceedings of the 3rd Workshop on Middleware for Context-Aware Applications in the IoT
影响因子：
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作者：
G. Stefano;A. Petricola;C. Zaroliagis
通讯作者：
C. Zaroliagis