Demo: visual mimo based LED - camera communication applied to automobile safety

The inherent limitations in RF spectrum availability and susceptibility to interference make it difficult to meet the reliability required for automotive safety applications. To address this challenge, this work explores an alternative communication system called Visual MIMO that uses light emitting arrays as transmitters and cameras as receivers. Visual MIMO applied to vehicular communication proposes to reuse existing LED rear and headlights as transmitters and existing cameras (e.g. those used for parking assistance, rear-view cameras) as receivers. In this work we show a proof of concept based demonstration of the Visual MIMO system consisting of an LED transmitter array and a highspeed camera

Virtual Trip Lines for Distributed Privacy-Preserving Traffic Monitoring

Automotive traffic monitoring using probe vehicles with Global Positioning System receivers promises significant improvements in cost, coverage, and accuracy. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we propose a system based on virtual trip lines and an associated cloaking technique. Virtual trip lines are geographic markers that indicate where vehicles should provide location updates. These markers can be placed to avoid particularly privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus they facilitate the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GP

Advances and open problems in federated learning

Abstract Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges