Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities

TL;DR

This paper introduces a novel federated learning framework for mobile crowdsensing that enhances privacy and diversifies city representations, demonstrated through improving vision algorithms for obstacle detection to aid visually impaired navigation.

Contribution

It presents the first federated learning crowdsensing framework aimed at diversifying urban data representation while preserving user privacy.

Findings

Successfully diversified city data representations.

Enhanced vision algorithms for obstacle detection.

Maintained privacy in crowdsensing data collection.

Abstract

Mobile Crowdsensing has become main stream paradigm for researchers to collect behavioral data from citizens in large scales. This valuable data can be leveraged to create centralized repositories that can be used to train advanced Artificial Intelligent (AI) models for various services that benefit society in all aspects. Although decades of research has explored the viability of Mobile Crowdsensing in terms of incentives and many attempts have been made to reduce the participation barriers, the overshadowing privacy concerns regarding sharing personal data still remain. Recently a new pathway has emerged to enable to shift MCS paradigm towards a more privacy-preserving collaborative learning, namely Federated Learning. In this paper, we posit a first of its kind framework for this emerging paradigm. We demonstrate the functionalities of our framework through a case study of diversifying two vision algorithms through to learn the representation of ordinary sidewalk obstacles as part of enhancing visually impaired navigation.