FL-NoiseMap: A Federated Learning-based privacy-preserving Urban Noise-Pollution Measurement System

Abstract Increasing levels of noise pollution in urban environments are a primary cause of various physical and psychological health issues. There is an urgent requirement to manage environmental noise by assessing the current levels of noise pollution by gathering real-world data and building a fine-granularity real-time noise map. Traditionally, simulation-based, small-scale sensor-network-based, and participatory sensing-based approaches have been used to estimate noise levels in urban areas. These techniques are inadequate to gauge the prevalence of noise pollution in urban areas and have been shown to leak private user data. This paper proposes a novel federated learning-based urban noise mapping system, FL-NoiseMap, that significantly enhances the privacy of participating users without adversely affecting the application performance. We list several state-of-the-art urban noise monitoring systems that can be seamlessly ported to the federated learning-based paradigm and show that the existing privacy-preserving approaches can be used as an add-on to enhance participants’ privacy. Moreover, we design an “m-hop” application model modification approach for privacy preservation, unique to FL-NoiseMap. We also describe techniques to maintain data reliability for the proposed application. Numerical experiments on simulated datasets showcase the superiority of the proposed scheme in terms of users’ privacy preservation and noise map reliability. The proposed scheme achieves the lowest average normalized root mean square error in the range of 4% to 7% as the number of participants varies between 500 and 5000 while providing maximum coverage of over 95% among various competing algorithms. The proposed malicious contribution removal framework can decrease the average normalizedroot mean square error by more than 50% for simulations having up to 20% malicious users.