# On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator

arXiv: https://arxiv.org/abs/2108.02712 · HTML: https://mlsanity.com/paper/2108.02712/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 25 of 25)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism](https://mlsanity.com/paper/1911.05642/) (2019)
- [Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach](https://mlsanity.com/paper/1905.07479/) (2019)
- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Estimation of Individual Device Contributions for Incentivizing Federated Learning](https://mlsanity.com/paper/2009.09371/) (2020)
- [Mechanism Design for Multi-Party Machine Learning](https://mlsanity.com/paper/2001.08996/) (2020)
- [Modeling the Evolution of Line-of-Sight Blockage for V2V Channels](https://mlsanity.com/paper/1607.04420/) (2016)
- [Dynamic Standalone Drone-Mounted Small Cells](https://mlsanity.com/paper/2009.02192/) (2020)
- [Task Allocation for Asynchronous Mobile Edge Learning with Delay and Energy Constraints](https://mlsanity.com/paper/2012.00143/) (2020)
- [Standalone Deployment of a Dynamic Drone Cell for Wireless Connectivity of Two Services](https://mlsanity.com/paper/2101.09701/) (2021)
- [Energy Efficient Altitude Optimization of an Aerial Access Point](https://mlsanity.com/paper/2103.14952/) (2021)

## Built on by (most cited first, 6 of 6)

- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning](https://mlsanity.com/paper/2308.06267/) (2023)
- [FedBA: Non-IID Federated Learning Framework in UAV Networks](https://mlsanity.com/paper/2210.04699/) (2022)
- [FedEGG: Federated Learning with Explicit Global Guidance](https://mlsanity.com/paper/2404.11888/) (2024)
- [Enabling Federated Object Detection for Connected Autonomous Vehicles: A Deployment-Oriented Evaluation](https://mlsanity.com/paper/2509.01868/) (2025)

## Abstract (arXiv)

In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also acts as an orchestrator. Expecting non-IID data distribution, we focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle. This can be done through proper allocation of the wireless resources for addressing learner and data heterogeneity. Thus, we propose a reactive method for the allocation of wireless resources, that happens dynamically each FL round, and is based on each learner's contribution to the general model. In addition to this, we explore the use of static methods that remain constant across all rounds. Since we expect partial work from each learner, we use the FedProx FL algorithm, in the task of computer vision. For testing, we construct a non-IID data distribution of the MNIST and FMNIST datasets among four types of learners, in scenarios that represent the quickly changing environment. The results show that proactive measures are effective and versatile at improving system accuracy, and quickly learning the CO class when underrepresented in the network. Furthermore, the experiments show a tradeoff between FedProx intensity and resource allocation efforts. Nonetheless, a well adjusted FedProx local optimizer allows for an even better overall accuracy, particularly when using deeper neural network (NN) implementations.