# A Federated Learning-enabled Smart Street Light Monitoring Application: Benefits and Future Challenges

arXiv: https://arxiv.org/abs/2208.12996 · HTML: https://mlsanity.com/paper/2208.12996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 27 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Personalized Cross-Silo Federated Learning on Non-IID Data](https://mlsanity.com/paper/2007.03797/) (2020)
- [Federated Meta-Learning with Fast Convergence and Efficient Communication](https://mlsanity.com/paper/1802.07876/) (2018)
- [Personalization of Deep Learning](https://mlsanity.com/paper/1909.02803/) (2019)
- [Dynamic Sampling and Selective Masking for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2003.09603/) (2020)
- [Distributed deep learning on edge-devices: feasibility via adaptive compression](https://mlsanity.com/paper/1702.04683/) (2017)
- [IoT Connectivity Technologies and Applications: A Survey](https://mlsanity.com/paper/2002.12646/) (2020)
- [A Dataset of Images of Public Streetlights with Operational Monitoring using Computer Vision Techniques](https://mlsanity.com/paper/2203.16915/) (2022)

## Abstract (arXiv)

Data-enabled cities are recently accelerated and enhanced with automated learning for improved Smart Cities applications. In the context of an Internet of Things (IoT) ecosystem, the data communication is frequently costly, inefficient, not scalable and lacks security. Federated Learning (FL) plays a pivotal role in providing privacy-preserving and communication efficient Machine Learning (ML) frameworks. In this paper we evaluate the feasibility of FL in the context of a Smart Cities Street Light Monitoring application. FL is evaluated against benchmarks of centralised and (fully) personalised machine learning techniques for the classification task of the lampposts operation. Incorporating FL in such a scenario shows minimal performance reduction in terms of the classification task, but huge improvements in the communication cost and the privacy preserving. These outcomes strengthen FL's viability and potential for IoT applications.