# B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction

arXiv: https://arxiv.org/abs/2310.14669 · HTML: https://mlsanity.com/paper/2310.14669/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 23 Oct 2023.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [A Survey of Model Compression and Acceleration for Deep Neural Networks](https://mlsanity.com/paper/1710.09282/) (2017)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach](https://mlsanity.com/paper/2003.08725/) (2020)
- [Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation](https://mlsanity.com/paper/2101.06905/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Short-term Traffic Prediction with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.00712/) (2020)
- [BFRT: Blockchained Federated Learning for Real-time Traffic Flow Prediction](https://mlsanity.com/paper/2305.17677/) (2023)
- [A Location-based and Hierarchical Framework for Fast Consensus in Blockchain Networks](https://mlsanity.com/paper/2305.16962/) (2023)
- [A Hierarchical and Location-aware Consensus Protocol for IoT-Blockchain Applications](https://mlsanity.com/paper/2305.17681/) (2023)
- [A Hybrid Blockchain-Edge Architecture for Electronic Health Records Management with Attribute-based Cryptographic Mechanisms](https://mlsanity.com/paper/2305.19797/) (2023)
- [Aggregated Zero-knowledge Proof and Blockchain-Empowered Authentication for Autonomous Truck Platooning](https://mlsanity.com/paper/2305.19813/) (2023)

## Abstract (arXiv)

Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This article proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.