Direct Acyclic Graph Blockchain-based Personalized Federated Mutual Distillation Learning in Internet of Vehicles

HUANG Xiaoge; WU Yuhang; YIN Hongbo; LIANG Chengchao; CHEN Qianbin

doi:10.11999/JEIT230976

Volume 46 Issue 7

Jul. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(7): 2821-2830

HUANG Xiaoge, WU Yuhang, YIN Hongbo, LIANG Chengchao, CHEN Qianbin. Direct Acyclic Graph Blockchain-based Personalized Federated Mutual Distillation Learning in Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2821-2830. doi: 10.11999/JEIT230976

Citation:

HUANG Xiaoge, WU Yuhang, YIN Hongbo, LIANG Chengchao, CHEN Qianbin. Direct Acyclic Graph Blockchain-based Personalized Federated Mutual Distillation Learning in Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2821-2830. doi: 10.11999/JEIT230976

Citation:

HUANG Xiaoge, WU Yuhang, YIN Hongbo, LIANG Chengchao, CHEN Qianbin. Direct Acyclic Graph Blockchain-based Personalized Federated Mutual Distillation Learning in Internet of Vehicles[J]. Journal of Electronics & Information Technology, 2024, 46(7): 2821-2830. doi: 10.11999/JEIT230976

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Direct Acyclic Graph Blockchain-based Personalized Federated Mutual Distillation Learning in Internet of Vehicles

doi: 10.11999/JEIT230976

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62371082,62001076), The General Program of Natural Science Foundation of Chongqing (CSTB2023NSCQ-MSX0726,cstc2020jcyj-msxmX0878)

Received Date: 2023-09-06
Rev Recd Date: 2024-04-16

Available Online: 2024-05-12

Publish Date: 2024-07-29

Abstract

Abstract

Federated Learning (FL) emerges as a distributed training method in the Internet of Vehicle (IoV), allowing Connected and Automated Vehicles (CAVs) to train a global model by exchanging models instead of raw data, protecting data privacy. Due to the limitation of model accuracy and communication overhead in FL, in this paper, a Directed Acyclic Graph (DAG) blockchain-based IoV is proposed that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-based Asynchronous Federated Mutual distillation Learning (DAFML) algorithm is introduced to improve the model performance, which utilizes a teacher model and a student model to mutual distillation in the local training. Specifically, the teacher model with a professional network could achieve higher model accuracy, while the student model with a lightweight network could reduce the communication overhead in contrast. Moreover, to further improve the model accuracy, the personalized weight based on global epoch and model accuracy is designed to adjust the mutual distillation in the model updating. Simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy and distillation ratio.
- Federated Learning (FL),
- Mutual distillation,
- Direct Acyclic Graph (DAG),
- Personalized weight

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References(16)

References

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