# A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles

arXiv: https://arxiv.org/abs/2108.03981 · HTML: https://mlsanity.com/paper/2108.03981/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning](https://mlsanity.com/paper/1907.06290/) (2019)
- [Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications](https://mlsanity.com/paper/2002.05485/) (2020)

## Built on by (most cited first, 4 of 4)

- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [Federated Learning-based Vehicle Trajectory Prediction against Cyberattacks](https://mlsanity.com/paper/2306.08566/) (2023)
- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [Enhanced Battery Capacity Estimation in Data-Limited Scenarios through Swarm Learning](https://mlsanity.com/paper/2504.12444/) (2025)

## Abstract (arXiv)

Federated Deep Learning (FDL) is helping to realize distributed machine learning in the Internet of Vehicles (IoV). However, FDL's global model needs multiple clients to upload learning model parameters, thus still existing unavoidable communication overhead and data privacy risks. The recently proposed Swarm Learning (SL) provides a decentralized machine-learning approach uniting edge computing and blockchain-based coordination without the need for a central coordinator. This paper proposes a Swarm-Federated Deep Learning framework in the IoV system (IoV-SFDL) that integrates SL into the FDL framework. The IoV-SFDL organizes vehicles to generate local SL models with adjacent vehicles based on the blockchain empowered SL, then aggregates the global FDL model among different SL groups with a proposed credibility weights prediction algorithm. Extensive experimental results demonstrate that compared with the baseline frameworks, the proposed IoV-SFDL framework achieves a 16.72% reduction in edge-to-global communication overhead while improving about 5.02% in model performance with the same training iterations.