# Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles

arXiv: https://arxiv.org/abs/2209.10722 · HTML: https://mlsanity.com/paper/2209.10722/

## Key facts

- Cited by 1 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 22 Sep 2022.

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

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [A Blockchain-based Decentralized Federated Learning Framework with Committee Consensus](https://mlsanity.com/paper/2004.00773/) (2020)
- [Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks](https://mlsanity.com/paper/1912.13163/) (2019)
- [Decentralized Federated Learning: A Segmented Gossip Approach](https://mlsanity.com/paper/1908.07782/) (2019)
- [Decentralized Federated Learning: Balancing Communication and Computing Costs](https://mlsanity.com/paper/2107.12048/) (2021)
- [Communication-Efficient Algorithms for Decentralized and Stochastic Optimization](https://mlsanity.com/paper/1701.03961/) (2017)
- [Edge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions](https://mlsanity.com/paper/2012.06992/) (2020)
- [A Discrete-event-based Simulator for Distributed Deep Learning](https://mlsanity.com/paper/2112.00952/) (2021)

## Built on by (most cited first, 1 of 1)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)

## Abstract (arXiv)

Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks' weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the limitation of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters, leading to the drawback of a single point of failure and scaling issues for increasing V2X network size. Meanwhile, in intelligent transport scenarios, data collected from onboard sensors are redundant, which degrades the performance of aggregation. To tackle these problems, we explore a novel idea of decentralized data processing and introduce a federated learning framework for in-network vehicles, C-DFL(Consensus based Decentralized Federated Learning), to tackle federated learning on connected vehicles and improve learning quality. Extensive simulations have been implemented to evaluate the performance of C-DFL, that demonstrates C-DFL outperforms the performance of conventional methods in all cases.