Situational Collective Perception: Adaptive and Efficient Collective Perception in Future Vehicular Systems
Ahmad Syazli Mohd Khalil,Tobias Meuser,Yassin Alkhalili,Antonio Fernandez,Lukas Stäcker,Ralf Steinmetz +5 more
- pp 346-352
Reads0
Chats0
TLDR
inspired by the Federated Learning (FL) approach, this work tailor a collective perception architecture, introducing Situational Collective Perception (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective perception in future vehicular networks.Abstract:
: With the emerge of Vehicle-to-everything (V2X) communication, vehicles and other road users can perform Collective Perception (CP), whereby they exchange their individually detected environment to increase the collective awareness of the surrounding environment. To detect and classify the surrounding environmental objects, preprocessed sensor data (e.g., point-cloud data generated by a Lidar) in each vehicle is fed and classified by onboard Deep Neural Networks (DNNs). The main weakness of these DNNs is that they are commonly statically trained with context-agnostic data sets, limiting their adapt-ability to specific environments. This may eventually prevent the detection of objects, causing safety disasters. Inspired by the Federated Learning (FL) approach, in this work we tailor a collective perception architecture, introducing Situational Collective Perception (SCP) based on dynamically trained and situational DNNs, and enabling adaptive and efficient collective perception in future vehicular networks.read more
Citations
More filters
Proceedings ArticleDOI
Dependability: Enablers in 5G Campus Networks for Industry 4.0
Ahmad Syazli Mohd Khalil,Benjamin Becker,Lisa Wernet,Ralf Kundel,Björn Richerzhagen,Tobias Meuser,Ralf Steinmetz +6 more
TL;DR: In this paper , the authors focus on an analysis of 5G campus networks and their dependability as prominent candidates for wireless connectivity in smart factories and provide an overview of modern smart factories.
Proceedings ArticleDOI
Dependability: Enablers in 5G Campus Networks for Industry 4.0
TL;DR: In this paper , the authors focus on an analysis of 5G campus networks and their dependability as prominent candidates for wireless connectivity in smart factories and provide an overview of modern smart factories.
References
More filters
Posted Content
Federated Learning: Strategies for Improving Communication Efficiency
Jakub Konečný,H. Brendan McMahan,Felix X. Yu,Peter Richtárik,Ananda Theertha Suresh,Dave Bacon +5 more
TL;DR: Two ways to reduce the uplink communication costs are proposed: structured updates, where the user directly learns an update from a restricted space parametrized using a smaller number of variables, e.g. either low-rank or a random mask; and sketched updates, which learn a full model update and then compress it using a combination of quantization, random rotations, and subsampling.
Journal ArticleDOI
Federated Learning: Challenges, Methods, and Future Directions
TL;DR: In this paper, the authors discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.
Proceedings ArticleDOI
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
TL;DR: The reasons why deep learning models may leak information about their training data are investigated and new algorithms tailored to the white-box setting are designed by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks.
Journal ArticleDOI
Federated Learning: Collaborative Machine Learning without Centralized Training Data
TL;DR: Federated learning allows several actors to collaborate on the development of a single, robust machine learning model without sharing data, allowing crucial issues such as data privacy, data security, data access rights, and access to heterogeneous data to be addressed.
Journal ArticleDOI
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues
TL;DR: The significance and technical challenges of applying FL in vehicular IoT, and future research directions are discussed, and a brief survey of existing studies on FL and its use in wireless IoT is conducted.
Related Papers (5)
Effects of Perceptual Augmentation Of Visual Displays: Dissociation of Performance and Situational Awareness
R. Jay Shively,Allen Goodman +1 more