首页|New Findings from University of Victoria Update Understanding of Machine Learnin g (Crs:a Privacy-preserving Two-layered Distributed Machine Learning Framework for Iov)
New Findings from University of Victoria Update Understanding of Machine Learnin g (Crs:a Privacy-preserving Two-layered Distributed Machine Learning Framework for Iov)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published.According to news reporting originating from Victoria,Canada,by NewsRx correspondents,research stated,"Nowadays,vehicles can provi de many valuable data (such as the videos recorded by dashcams) for analytical m odel building.Integrating vehicular ad hoc networks with the Internet of Things (IoT),the Internet of Vehicles (IoV) has a promising future." Financial support for this research came from Natural Sciences and Engineering R esearch Council of Canada (NSERC).Our news editors obtained a quote from the research from the University of Victo ria,"In IoV,vehicles maintain their own communication,computing,and learning capabilities.Thus,instead of sending the data to a central server for model t raining,which leads to a high communication overhead,vehicles can train the da ta locally.However,it is still a challenge to preserve the privacy while keepi ng both the communication and computation overheads of vehicles acceptable.In t his article,we present a distributed machine learning framework with a two-laye red architecture.The architecture uniquely involves vehicle clusters,roadside units,and a central server,which provides a basic guarantee to the vehicle pri vacy and also limits the overhead.By carefully adopting cryptographic tools and techniques,the framework has the following properties:1) it preserves the pri vacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the a pplication layer; 4) the evaluation shows that it is lightweight for vehicles."
VictoriaCanadaNorth and Central Amer icaCyborgsEmerging TechnologiesMachine LearningUniversity of Victoria
NETL
NSTL
