# Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS

arXiv: https://arxiv.org/abs/2204.00215 · HTML: https://mlsanity.com/paper/2204.00215/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [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 Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2006.01412/) (2020)
- [Model-Agnostic Multi-Agent Perception Framework](https://mlsanity.com/paper/2203.13168/) (2022)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Intelligent Transportation Systems Using External Infrastructure: A Literature Survey](https://mlsanity.com/paper/2112.05615/) (2021)

## Built on by (most cited first, 7 of 7)

- [Bridging the Domain Gap for Multi-Agent Perception](https://mlsanity.com/paper/2210.08451/) (2022)
- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception](https://mlsanity.com/paper/2209.13679/) (2022)
- [FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems](https://mlsanity.com/paper/2304.01534/) (2023)
- [On the Federated Learning Framework for Cooperative Perception](https://mlsanity.com/paper/2404.17147/) (2024)
- [Accurate non-stationary short-term traffic flow prediction method](https://mlsanity.com/paper/2205.00517/) (2022)
- [A V2X-based Privacy Preserving Federated Measuring and Learning System](https://mlsanity.com/paper/2401.13848/) (2024)

## Abstract (arXiv)

Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrastructure. In today's C-ITS, smart traffic participants are capable of timely generating and transmitting a large amount of data. However, these data can not be used for model training directly due to privacy constraints. In this paper, we introduce a federated learning framework coping with Hierarchical Heterogeneity (H2-Fed), which can notably enhance the conventional pre-trained deep learning model. The framework exploits data from connected public traffic agents in vehicular networks without affecting user data privacy. By coordinating existing traffic infrastructure, including roadside units and road traffic clouds, the model parameters are efficiently disseminated by vehicular communications and hierarchically aggregated. Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i.e., aggregation in layers of roadside units and cloud. The experiment results indicate that our method can well balance the learning accuracy and stability according to the knowledge of heterogeneity in current communication networks. Comparing to other baseline approaches, the evaluation on federated datasets shows that our framework is more general and capable especially in application scenarios with low communication quality. Even when 90% of the agents are timely disconnected, the pre-trained deep learning model can still be forced to converge stably, and its accuracy can be enhanced from 68% to over 90% after convergence.