# Mobility-Aware Decentralized Federated Learning with Joint Optimization of Local Iteration and Leader Selection for Vehicular Networks

arXiv: https://arxiv.org/abs/2503.06443 · HTML: https://mlsanity.com/paper/2503.06443/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2025.

## Built on by (most cited first, 5 of 5)

- [Open-Source LLM-Driven Federated Transformer for Predictive IoV Management](https://mlsanity.com/paper/2505.00651/) (2025)
- [Privacy-Preserving Federated Learning for Fair and Efficient Urban Traffic Optimization](https://mlsanity.com/paper/2511.06363/) (2025)
- [Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregation](https://mlsanity.com/paper/2606.10774/) (2026)
- [Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints](https://mlsanity.com/paper/2606.28342/) (2026)
- [PROSE: A Theory of Optimal Stopping with Perishable Evidence for Peer Selection in Intermittently Connected Decentralised Learning](https://mlsanity.com/paper/2609.23845/) (2026)

## Abstract (arXiv)

Federated learning (FL) emerges as a promising approach to empower vehicular networks, composed by intelligent connected vehicles equipped with advanced sensing, computing, and communication capabilities. While previous studies have explored the application of FL in vehicular networks, they have largely overlooked the intricate challenges arising from the mobility of vehicles and resource constraints. In this paper, we propose a framework of mobility-aware decentralized federated learning (MDFL) for vehicular networks. In this framework, nearby vehicles train an FL model collaboratively, yet in a decentralized manner. We formulate a local iteration and leader selection joint optimization problem (LSOP) to improve the training efficiency of MDFL. For problem solving, we first reformulate LSOP as a decentralized partially observable Markov decision process (Dec-POMDP), and then develop an effective optimization algorithm based on multi-agent proximal policy optimization (MAPPO) to solve Dec-POMDP. Finally, we verify the performance of the proposed algorithm by comparing it with other algorithms.