# RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence

arXiv: https://arxiv.org/abs/2310.07268 · HTML: https://mlsanity.com/paper/2310.07268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 11 Oct 2023.

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

- [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 for Ultra-Reliable Low-Latency V2V Communications](https://mlsanity.com/paper/1805.09253/) (2018)
- [FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling](https://mlsanity.com/paper/2009.10748/) (2020)
- [Semi-Federated Learning](https://mlsanity.com/paper/2003.12795/) (2020)

## Abstract (arXiv)

Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated by state-of-the-art vehicular ad-hoc networks and artificial intelligence, various VCI applications come to place, e.g., collaborative sensing, positioning, and mapping. The collaborative property of VCI applications generally requires data to be shared among participants, thus forming network-wide intelligence. How to fulfill this process without compromising data privacy remains a challenging issue. Although federated learning (FL) is a promising tool to solve the problem, adapting conventional FL frameworks to VCI is nontrivial. First, the centralized model aggregation is unreliable in VCI because of the existence of stragglers with unfavorable channel conditions. Second, existing FL schemes are vulnerable to Non-IID data, which is intensified by the data heterogeneity in VCI. This paper proposes a novel federated learning framework called RaftFed to facilitate privacy-preserving VCI. The experimental results show that RaftFed performs better than baselines regarding communication overhead, model accuracy, and model convergence.