# FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

arXiv: https://arxiv.org/abs/2110.12876 · HTML: https://mlsanity.com/paper/2110.12876/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)

## Built on by (most cited first, 11 of 11)

- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing](https://mlsanity.com/paper/2111.06146/) (2021)
- [A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems](https://mlsanity.com/paper/2401.06831/) (2024)
- [Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach](https://mlsanity.com/paper/2401.09680/) (2024)
- [Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach](https://mlsanity.com/paper/2401.11836/) (2024)
- [SoK: Assessing the State of Applied Federated Machine Learning](https://mlsanity.com/paper/2308.02454/) (2023)
- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)

## Abstract (arXiv)

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw data. Furthermore, we investigate the management of Parked Vehicle assisted Edge Computing (PVEC) by FedParking. In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking. We formulate the interactions among the PLOs and vehicles as a multi-lead multi-follower Stackelberg game. Considering the dynamic arrivals of the vehicles and time-varying parking capacity constraints, we present a multi-agent deep reinforcement learning approach to gradually reach the Stackelberg equilibrium in a distributed yet privacy-preserving manner. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.