Advances in Privacy-Preserving Localization and Distributed Computing

The field of localization and distributed computing is moving towards a more privacy-preserving and efficient approach. Researchers are exploring the use of federated learning and distributed algorithms to enable secure and accurate localization and data processing. This shift is driven by the need to protect user privacy and reduce communication costs. Noteworthy papers in this area include Federated Item Response Theory Models, which proposes a novel framework for estimating traditional IRT models in a distributed manner without sacrificing accuracy or security. Another notable paper is INTACT, which introduces a compact storage technique for data streams in mobile devices to unlock user privacy at the edge. Other innovative works in this area focus on developing lightweight and efficient algorithms for indoor localization, such as RaGNNarok, which uses a graph neural network to enhance radar point clouds, and MGPRL, which employs distributed multi-Gaussian processes for Wi-Fi-based multi-robot relative localization.

Sources

Federated Item Response Theory Models

INTACT: Compact Storage of Data Streams in Mobile Devices to Unlock User Privacy at the Edge

Golden Ratio Assisted Localization for Wireless Sensor Network

MGPRL: Distributed Multi-Gaussian Processes for Wi-Fi-based Multi-Robot Relative Localization in Large Indoor Environments

RaGNNarok: A Light-Weight Graph Neural Network for Enhancing Radar Point Clouds on Unmanned Ground Vehicles

Cross-platform Smartphone Positioning at Museums

A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning

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